Non-invasive decoding of imagined movements has significant potential in developing neuro-prosthetics and assistive technology for people with motor disabilities. While there has been extensive research conducted on classifying bilateral movement imaginations, classification of the kinematics associated with movement imaginations of unilateral limb is still an open research problem. Decoding the movement imaginations of a single limb can enhance human computer interaction by enabling more natural and intuitive movement control. This work aims to classify the direction of imagined hand movement in a four directional motor imagery (MI) task. Electroencephalogram (EEG) is recorded from 14 healthy subjects while they imagined center out movement of their right hand in four orthogonal directions (right, left, up and down). A Wavelet Phase Common Spatial Pattern (WPCSP) method is proposed to extract useful features from EEG to decode the imagined movement directions. The proposed method extracts informative features from the instantaneous phase signal using Common Spatial Pattern and are then classified using Linear Discriminant classifier, resulting in a mean binary direction classification accuracy of 68.54±7.5% and four-class direction classification accuracy of 38.48±8.1% among 14 healthy subjects. The results highlight the significance of phase-based features in decoding imagined kinematics of unilateral limb movements. This outcome is a step towards achieving higher degrees of freedom of movement and enhancing the efficacy of rehabilitation strategies.

Motor imagery (MI)-based Brain-Computer Interface (BCI) systems decode the neuronal patterns during motor movement imagination tasks and induce neuroplasticity to assist patients with stroke rehabilitation. Deep Learning methods achieve better generalization but require a large dataset for which longer calibration sessions are required. Transfer Learning methods learn the shift in data distribution from source to the target domain without the need for a large dataset. The existing cross-subject and cross-session transfer learning methods learn the shift in input data distribution alone, ignoring label distribution. This work proposes a cross-task transfer learning to learn new MI tasks by realigning the parameters of the deep learning model. The parameters of the pre-trained EEGNet model, trained on binary MI tasks, are re-aligned to decode a new MI task using Conditional Domain Adversarial Network (CDAN), while improving the generalizability across existing and new MI tasks. The proposed method is evaluated on the BCI Competition IV 2a dataset and compared with the popular fine-tuning approach for transfer learning in deep learning. The classification results achieved using the proposed CDAN-based cross-task transfer learning approach generalize better compared to conventional training and the fine-tuning approaches for transfer learning. The proposed method achieved an improvement in classification accuracy of around 6% for learning a third MI task from a pre-trained binary MI task classifier using EEGNet. The proposed CDAN-based cross-task transfer learning approach can significantly reduce the calibration session to learn new MI tasks, as it offers to learn the shift in input and output label distribution due to new MI tasks. Further, the proposed method can be scaled to learn dexterous MI tasks and kinematics relation information from MI tasks.

Motor Imagery (MI)-based Brain-Computer Interface (MI-BCI) systems induce neuroplasticity, promoting rehabilitation in stroke-affected patients. Decoding of kinematics information such as speed from unilateral hand MI tasks provides more natural control of the BCI systems. However, decoding speed related information from unilateral MI tasks is challenging due to the significant spatial overlap of neuronal sources and the inherently low spatial resolution of EEG. To address this, we propose EEGScaler, an end-to-end deep learning framework designed to decode slow v/s fast MI tasks by adaptively scaling EEG samples and electrodes with high discriminative value. The work proposes electrode-scaling and sample-scaling blocks to learn the importance of electrodes (i.e., spatial) and samples (i.e., temporal) in decoding speed from MI tasks. Further, spatiotemporal features are extracted using temporal and depth-wise convolution filters. In this work, subject-independent data is used to learn the weights of the EEGScaler and then subject-specific data is used to fine-tune the weights of the proposed electrode-scaling and sample-scaling blocks. The proposed method is implemented on 14 healthy subjects’ EEG data to classify slow v/s fast MI tasks performed using their dominant hand. The cross-validated subject-specific classification accuracy achieved using the proposed method outperformed the existing methods by ~7%. EEGScaler is a novel end-to-end learning model designed to assign importance to spatial and temporal information in EEG via electrode- and sampling-scaling blocks, respectively. Decoding of kinematics such as speed of MI tasks increases the degree of freedom in BCI systems, paving the way for more intuitive and efficient neurorehabilitation applications. This advancement has the potential to improve motor rehabilitation strategies by enabling more precise and adaptive BCI-driven therapy tailored to individual recovery needs.

Attention is a critical cognitive function in daily life, and its impairment can lead to serious consequences. Electroencephalogram (EEG) enables detection of brain activity related to attention, but decoding attentional states from EEG signals remains challenging due to noise artefacts and low spatial resolution. While deep learning approaches have shown promise, their generalizability across subjects remain limited. This work proposes a novel end-to-end hybrid deep learning architecture, STESA-Net, for classifying attention versus inattention states from multichannel EEG signal. STESA-Net integrates spatiotemporal convolutional layers, self-attention mechanism, and Bidirectional Long Short-Term Memory (Bi-LSTM) units to effectively capture and weight relevant spatial and temporal features. The design enhances the model’s ability to learn subject-specific patterns while maintaining generalizability across individuals. The proposed method is validated using 11 subjects EEG data recorded during a simulated driving task, employing leave-one-out-validation (LOOV) method. The proposed method achieved an average attention v/s inattention classification accuracy, precision, recall and F1 score of 78. 47%, 80. 16%, 78. 46%, and 78. 14%, respectively. The improvement in average classification accuracy achieved by the proposed method over the existing methods is ~7%. The results highlight the effectiveness of combining convolutional layers, self-attention mechanisms, and Bi-LSTM networks for robust cross-subject attentional state classification. The proposed framework offers a promising solution for real-world applications in attention monitoring and may aid the early detection of attention-related cognitive impairments with reduced computational overhead and enhanced generalization.

Motor Imagery-based Brain Computer Interface (BCI) system, that decodes imagined movements from non-invasive electroencephalogram (EEG) are of significant importance, as it can enhance neurorehabilitation and human-computer interaction. Decoding the kinematic parameters of imagined movement is essential to realize BCI systems with higher degrees of freedom of movement and precise control over external effectors. In this work, we propose an efficient algorithm to decode imagined bi-directional movements of hand at two different speeds, slow and fast, from EEG-based BCI. EEG is recorded from fourteen healthy subjects, while they imagined slow and fast movement of their right hand towards the right or left direction. Wavelet-Common Spatial Pattern (WCSP) features and Movement Related Cortical Potential (MRCP) features are extracted from the EEG and a subset of these features are further identified based on the subject-specificity of discriminative subbands and channels. Selected WCSP and MRCP features are then concatenated and used to decode the imagined slow and fast bi-directional movements. Binary classification of the speed-direction pairs resulted in an average classification accuracy of 68.1% across fourteen subjects. To our knowledge, this is the first work addressing decoding of imagined speed and direction of hand movements using EEG-BCI.

The burden of cognitive impairment is increasing worldwide and the rehabilitation of patients with such disorders is a major concern. Neurofeedback training (NFT) is emerging as a promising non-pharmacological intervention for enhancing cognition in healthy and cognitive-deficit patients. In this paper, we examine the efficacy of a real-time neurofeedback game using a four-channel consumer-grade Electroencephalography (EEG) system on enhancing overt and covert attention skills in Stroke and Mild cognitive impairment (MCI) patients. The game works based on navigating a car on a computer screen using the player's attention level computed from EEG signals streamed in real-time. The proposed NFT is conducted across 18 patients (10 Stroke and 8 MCI). The attention score significantly improved from the first session to the final session by 1.25-12.55% in the stroke group and 1.84-44.14% in the MCI group. The experimental results demonstrate that the proposed neurofeedback game using consumer-grade EEG is an effective tool for enhancing overt and covert attention skills in patients with cognitive impairment. To our knowledge, this is the first EEG-Brain Computer Interface study for improving overt and covert attention on the cross-sectional stroke-MCI patients.

With the recent outbreak of COVID-19, ultrasound is fast becoming an inevitable diagnostic tool for regular and continuous monitoring of the lung. However, lung ultrasound (LUS) is unique in the perspective that, the artefacts created by acoustic wave propagation is aiding clinicians in diagnosis. In this work, a novel approach is presented to extract acoustic wave propagation driven features such as acoustic shadows, local phase-based feature symmetry, and integrated backscattering to automatically detect the pleura and to aid a pretrained neural network to classify the severity of lung infection based on the region below pleura. A detailed analysis of the proposed approach on LUS images over the infection to full recovery period of ten confirmed COVID-19 subjects across 400 videos shows an average five-fold cross-validation accuracy, sensitivity, and specificity of 97%, 92%, and 98% respectively over randomly selected 5000 frames. The results and analysis show that, when the input dataset is limited and diverse as in the case of COVID-19 pandemic, an aided effort of combining acoustic propagation-based features along with the gray scale images, as proposed in this work, improves the performance of the neural network significantly even when tested against a completely new data acquisition.

Brain-Computer Interface (BCI) systems decode specific neuronal signatures from the brain and can provide an alternative communication pathway for patients suffering from neuromuscular disorders. Decoding complex neuronal signatures such as direction, speed, orientation from Electroencephalogram (EEG) is challenging due to complex network connectivity in the brain. In the existing work, phase and amplitude-related features are used to decode the direction information during motor execution task. In this paper, we aim to decode the binary direction (left v/s right direction) during hand motor imagination task. We use the phase-related feature known as Wavelet-Phase Locking Value (W-PLV) to decode the hand motor imagery direction on calibration session data of 20 healthy subjects. Further, an online session is conducted on 13 subjects that use the trained model from the calibration session to decode imagined movement direction. The average classification accuracy achieved on calibration and online session data using phase related features (W-PLV) is 64. 39 ± 4. 23% and 58. 67 ± 4. 06%, respectively. The phase related features (W-PLV) achieve an improvement of 4. 82% and 3. 04% on calibration and online sessions, respectively compared to the amplitude related features alone. Online decoding of user’s intentions such as direction can aid in achieving more natural control of a BCI system.

Electroencephalogram (EEG) based brain–computer interface (BCI) is an augmented communication modality between the brain and computer that exclusively depends on noninvasively recorded neuro-electrical activity. To establish multiple control commands in BCI systems, it is essential to efficiently identify unique EEG activation patterns underlying multiple motor tasks. This article investigates the brain activation modulations elicited by imagined movement directions and the EEG features that characterize them. An experiment was conducted in which ten subjects imagined two-dimensional right-hand movement in the left, right, up, and down directions in the vertical plane. The frequency and phase characteristics of EEG rhythms are explored to identify the features that discriminate movement imagination toward multiple directions. The proposed decoding step consists of Fisher's analysis of instantaneous phase values to identify EEG channels with optimal discriminative information followed by extraction of phase-locking values and temporal-spatial features, which are then used for classification. The proposed approach offers significantly higher (p< 0.05) performance than the state-of-the-art method while employing subject-specific time segments and channels. Hence, an average accuracy of 69.25% is obtained for binary classification of all pairs of movement directions with a performance increment of 8.33% compared to the conventional approaches using fixed channel-time paradigm. The results illustrate the potential use of noninvasive EEG signals in decoding imagined movement kinematics, and further research is needed to investigate and optimize the separability in multiple directions.

Spectrum sensing is an efficient way to determine the spectrum availabilities over the frequency range of interest, aiding in improving the spectrum utilization in the cognitive radio (CR) systems. Conventional Nyquist multiband sensing entails higher computational capability for sampling, quantization, and subsequent processing, lending the approach infeasible for applications with limited power budgets. In this brief, a power-efficient spectrum-sensing technique is proposed, which explores an accuracy-complexity tradeoff. The presented spectrum-sensing architecture is based on 1-bit quantization at the CR receiver and implements it in hardware by a resource- and power-efficient approach, using a finite-impulse-response (FIR) filter-bank channelizer. The proposed scheme allows the complex operators like multipliers and quantizers to be replaced by the inverter logic and high-speed comparators, reducing the hardware complexity and power consumption. We validate the proposed scheme on a field-programmable gate-array (FPGA) emulator for an aeronautical L-band digital aeronautical communication system (LDACS) application, and our results show that the proposed scheme achieves substantial resource reduction with at most 5% degradation in the detection accuracy in this case.

Continuous growth of air-traffic demands new technologies for future aeronautical communication systems. L-band Digital Aeronautical Communications System (LDACS) is considered as a potential candidate for the future air-to-ground links. Spectrum utilization efficiency of LDACS can be improved by enabling dynamic spectrum access (DSA) in the system. In this context, the present paper proposes a novel cyclostationary feature detection scheme based on the periodicity of the autocorrelation of LDACS signals for spectrum sensing, which is key requirement in DSA. A wideband spectrum sensing simulation model is developed to evaluate the detection performance of the proposed scheme. To improve the adaptability of the spectrum sensing unit to variable wide bandwidth, the proposed system uses interpolation and masking based filters that can extract individual LDACS channels from the wide-band signals. Results of simulation studies show that the proposed scheme provides better detection accuracy against the conventional cyclostationary feature detection scheme and energy detection scheme, while maintaining the false alarm rates to an acceptable level which balances the overall throughput of the system.

To improve the spatial resolution of the electroen-cephalogram (EEG) signal, it is conventional to use a large number of scalp electrode while recording oscillatory rhythms in motor imagery based brain-computer interface (MI-BCI). However, this increases the dimension of data and might fail to generalize and thus over-fit. Therefore, it is required to reduce the dimension of input data in an optimal way. In this paper, we propose a method using an artificial neural network to reduce the dimension of EEG for MI-BCI. We train an under-complete sparse autoencoder neural network for each subject separately to encode the EEG data optimally. The optimally encoded EEG trials are then used by the Filter Bank Common Spatial Pattern (FBCSP) method to decode the imagined motor movement. In similar lines, autoencoder was trained on subject independent data. We achieved improved motor imagery classification accuracy when the dimension of the data was almost reduced by half compared to the state-of art FBCSP. The performance of the proposed method is also compared with Sparse Common Spatial Pattern (SCSP) based channel selection method. The average classification accuracy obtained for 10 subjects is 74.3±8.06 % with only 13 encoded channels. Also, for the autoencoder trained to be subject independent we obtained an average classification accuracy of 66.64±3.93% with only 11 encoded channels after cross-validation. The study extends the use of autoencoder neural networks in motor imagery based brain-computer interface and shows significant improvement in performance with reduced data dimension.

Brain-Computer Interface (BCI) systems translate the users intentions coded by brain activity measures into actions through a control signal without using activity of any muscles or peripheral nerves. Usually, in Electroencephalography (EEG) based BCI experiment protocols, different mental tasks are performed to elicit unique brain signal responses, which are recognized by signal processing and machine learning methods. The work presented in this paper extracts the EEG phase synchrony feature called Phase Lock Value (PLV) to decode Motor Imagery (MI) of center-out hand movement in right and left directions. At first, PLV features of all the EEG channel pairs are extracted to detect the level of synchronization corresponding to the directional hand movements. The most significant channel pairs selected from direction-specific EEG signals corresponding to the imagined hand movement showed characteristic changes in PLV features. Mean Percentage Difference of PLV features are calculated and compared in different frequency bands to identify the most discriminative frequency band for the hand movement classification. The extracted PLV features offer 5.34% improvement in classification accuracy for the 7 best performing subjects compared to the relevant method in literature.

Electroencephalography (EEG)-based Brain Computer Interface has been used for image and voice familiarity detection in earlier works. It is known that efficient feature extraction is the key to a good classification accuracy in BCI systems. EEG discriminative features between familiar and unfamiliar names of people, presented in text format as visual stimuli, have not been reported in literature. Detection of familiar and unfamiliar names can play a distinct role in psychology, clinical assessment of cognitive impairment, lie detection, investigations and interrogations. In this paper, the subject's familiarity to names of people was studied based on EEG signals obtained while the subjects were exposed to familiar and unfamiliar names as a visual stimulus in the form of text. The discriminative features were found to be subject-specific. Using three subject-specific features, an average classification accuracy of 84.37% for familiar vs. unfamiliar names was achieved among 8 subjects.

Detection and classification of sleep and awake of an individual has potential applications in biomedical engineering, high-risk work places, vigilance monitoring in Advanced Driver Assistance Systems, etc. In this paper we present a method to classify sleep and awake states using Electroencephalogram (EEG) signal. The proposed method makes use of sample entropy measure and band power ratio of EEG signal as suitable features for efficient classification. The classification is performed using Support Vector Machine (SVM) and an average classification accuracy of 96.28% is obtained on performing the classification among 30 subjects.

Electroencephalogram (EEG) is one the most commonly used brain activity measurement tools in Brain-Computer Interface (BCI) framework to establish “thought” based human-machine interaction. On account of the fact that mental rehearsal of motor movement termed motor imagery, is a robust candidate for developing BCI systems, it is of great importance to find the distinction between the neural correlates of executed motor movement and its imagination, to enhance command signal generation and utilization in BCI-based communication and control. This paper investigates the EEG-based parameters associated with execution and imagination of left and right hand movement from a set of 10 healthy subjects. The study explores the distinguishable power and phase features in theta (4-8 Hz), alpha/mu (8-12 Hz), lower beta (12-20 Hz), higher beta (20-30 Hz) and gamma (30-40 Hz) bands of EEG. It is observed that the most informative channels that effectively discriminates motor execution and imagination varies across bands and tasks. Frontal and parietal channels are found to be more discriminative compared to those in central region in both tasks. Combining the task-specific discriminative features along with Common Spatial Pattern (CSP) based spatial features, an average classification accuracy of 81% is achieved in 5 ×5-fold cross validation, over 10 subjects. Further study is essential to investigate the temporal structure of the distinguishable features, to generalize the discriminative spectral and spatial characteristics and to improve the classification accuracy.

Phase synchronies are often used to study relationships between different parts of the brain and to identify regions that interact in a coordinated manner for a certain task. In this paper, we propose a wavelet reconstruction and phase-locking-based feature extraction method to visualize and classify the direction-specific phase synchronies between Electroencephalogram (EEG) channel-pairs for hand movements in 4 directions using EEG data collected from 7 subjects performing right hand movements. We then study its discriminative ability by using statistical analysis and report the most informative, direction-specific channels and wavelet levels. Next, we show the discriminative performance of the proposed feature extraction method in the binary classification of 6 direction pairs. Subsequently, we use the Minimum Redundancy Maximum Relevance feature selection algorithm to select features which improved the classification accuracy of our proposed method by 4.39%. Thus, the results demonstrate the potential of proposed wavelet phase-locking method to extract movement direction related information from EEG.

Biometrie recognition of persons are widely explored nowadays to develop robust and trustworthy security systems. On account of the unique neural signature of each person, the brain activity recorded by Electroencephalogram (EEG) has recently been identified as a potential biometric trait. In this paper, we propose an online EEG-based biometric system which utilizes the activations of brain towards a set of subject-specific self-referential visual stimuli. The stimuli set consist of a number of self-face images and that of subject's relatives, which are expected to elicit brain activity in a subject-specific manner. The subject-specific biometric marker used in the proposed authentication system is the relative spectral activity of left and right hemisphere EEG in various frequency bands. Experimental analysis reveals utility of alpha and beta bands for the proposed approach, offering an average biometric recognition accuracy of 87.50% over 4 subjects. Results of the proposed methodology give insights for further research to examine the permanence and generalization of the proposed system in a larger group of subjects.

Recently, noninvasive brain stimulation is gaining significant attention in stroke rehabilitation. In this paper, we investigate the effects of transcranial direct current stimulation (tDCS) on the motor-imagery brain-computer interface (MI-BCI) performance of stroke patients. To this end, we processed the EEG data collected from a randomized control trial (RCT) study of 19 stroke patients grouped into tDCS and sham. An ensemble method for feature extraction is proposed in this study that combines shrinkage regularized Common Spatial Pattern (CSP) features from the sensor-space and the cortical source-space Electroencephalography (EEG) across ten rehabilitation sessions. The classification results of MI vs. Idle state in stroke patients show that the concatenated features from both the sensor- and source space EEG provided an average cross-validation accuracy of 64.5% which is statistically significant (p <; 0.001) compared to either using source or sensor-space features. Further, our findings suggest that the effect of tDCS on the stroke recovery is pronounced in subjects whose delta and alpha band power during the post-tDCS intervention is significantly higher as compared to before intervention. The group-averaged sLORETA activation results showed a significantly higher number of dipoles activated in the tDCS group as compared to sham. In summary, our study paves a new way to analyze the neural correlates of the MI-BCI performance for stroke rehabilitation.

The performance of classification of various mental states using Electroencephalography (EEG) is often limited by the lack of information regarding the most discriminative channels and frequency bands. The paper proposes a Canonical Correlation Analysis (CCA) of EEG recorded during bilateral imagined hand movement. CCA determines linear transformation of EEG that is maximally correlated with a transformed version of neural response to imagined movement. In the proposed method, the linear weights are identified for the spatial and spectral components of EEG signal. The study investigates how the CCA-based transformation of the signal improves discrimination between two movement classes. Further, two parallel CCA blocks, determine two transformations of EEG, one that maximizes correlation with the corresponding class, and other that minimizes the correlation with the mismatched class. Features derived using this approach are used for single-trial classification of bilateral imagined hand movement. Time-frequency patterns of EEG derived from the proposed approach illustrate their discriminative ability in mu and beta bands. An average classification accuracy of 61.12% (109 subjects) and 73.34% (Best 40 subjects) are obtained. The results indicate the scope of CCA to obtain time-frequency representations of EEG and for single-trial classification of motor imagery (MI).

Brain-Computer Interface (BCI) technology provides a new mode of direct communication between man and machine, replacing human's normal output control pathways of nerves and muscles. It embeds huge potential to be explored in medicine, rehabilitation and entertainment that can be used for the healthy as well as the disabled. In order to thoroughly exploit BCI's capabilities, efficient man-machine interaction has to be established by accurately identifying human intentions from brain waves. Electroencephalogram (EEG) is a strong tool in BCI research as it is a cheap and easy non-invasive recording methodology of brain activity encoded with human thoughts. This paper investigates an EEG dataset recorded from 85 healthy subjects performing imagination of motor movements, and highlights the necessity of selecting the subject-specific spatial and spectral features to optimize motor imagery recognition performance. Subject-specific spectral and spatial features associated with right and left hand motor imagery are identified on account of the discriminative weights of EEG signals recorded from motor cortex region. Discriminative capability has been estimated using the Fisher ratio values of each frequency component for each channel. Using the proposed hybrid subject-specific selection of channels and bands, the proposed BCI system is capable of offering comparable classification accuracy with the state of the art methodology which employs more number of channels and frequency bands.

Recent advances in EEG-based brain-computer interfaces (BCIs) have shown that brain signals can be used to decode arm movement intention and execution in multiple directions. Conventional approaches use sensor space EEG for classifying movement related tasks. Sensor-space EEG can reveal only limited information about the trivial but complex tasks that involve higher degrees of freedom of the movement. On the contrary, source space analysis is expected to provide more information about the neurophysiological mechanism relevant to the task. To this end, we propose a novel source-space feature extraction technique based on supervised locality sensitive Factor analysis which approximates the neurophysiological functioning of our experimental data in a better way than that of a solely data-driven approach. EEG recordings in the sensor space are transformed into source space using the Weighted Minimum Norm Estimate (wMNE) method. We show that for a multi-class classification problem of classifying the EEG of voluntary arm movement in 4 orthogonal directions, the source space features offer a significant improvement in the classification accuracy compared to sensor space features. One-versus-rest (OVR) approach is used for multiclass classification with Fisher's Linear Discriminant (FLD) as the primary classifier.

Brain-Computer Interface (BCI) is a direct communication pathway between brain and external devices bypassing the natural pathway of nerves and muscles. BCI enables an individual to send commands to a peripheral device using his brain activity. Electroencephalogram (EEG) is the most commonly used brain signal acquisition method as it is simple, economical and portable. Feasibility of detecting familiar vs non-familiar voice signals using EEG signals has been investigated in this paper. The results show that combination of features such as mobility and complexity of the signal gives an average accuracy of 72.2% in classifying between familiar and unfamiliar voices among 8 subjects.

Multi-disciplinary study of human computer interaction has provided significant impact in the fields of neural engineering, cognitive neuroscience, rehabilitation and brain-computer interaction. This paper evaluates the impact of neurofeedback in the context of a simple computer game controlled by attention based brain signals. The designed game protocol requires the player to memorize a set of numbers displayed in a matrix format, and to correctly fill the matrix using his attention based brain patterns. Attention level of the player, quantified using sample entropy values of Electroencephalogram (EEG) signals, is the core control parameter of the game. A comparative study using a single neurofeedback group and 2 control groups (each group consists of 8 subjects) has been carried out to examine the impact of neurofeedback on enhancing attention score and cognitive skill in the context of the attention-driven game. Experimental results explicitly demonstrate the significance and usefulness of neurofeedback in EEG based games.

Biometric recognition of persons using brain waves has been identified as an attractive topic of research today. Existing popular biometric modalities of face, finger prints and voice signals are vulnerable to various kinds of attacks and spoofing techniques, whereas the emerging biometric trait extracted from brain wave is expected to act as an ideal biometric feature offering high degree of uniqueness, stability and universality. This paper analyses the efficacy of the complexity of Electroencephalogram (EEG) signals recorded during rest state for recognizing individuals from a publicly available EEG dataset consisting of 109 subjects. Sample entropy features extracted from delta, theta, alpha, beta and gamma bands of 64 channel EEG have been evaluated for subject-identification in the proposed system. It is found that beta band entropy has the highest inter-subject variability. Based on a Mahalanobis distance based classifier, beta entropy gives an average correct recognition rate of 98.31%. It has also been observed that concatenation of entropy features with power spectral density (PSD) values improves the system performance. Further analysis is essential to investigate the stability of results over time and to optimize the recognition performance at a reduced number of channels.

Brain activities are inherently determined by a person's unique pattern of neural pathways and are closely associated with his/her genetic personality traits. Brain activity recorded by electroencephalogram (EEG), has recently been regarded as potential candidate in future generation biometric systems. In this paper, a biometric identification system is proposed, combining subject-specific alpha peak frequency, peak power and delta band power values to form discriminative feature vectors and templates. A public dataset of EEG signals recorded from 109 healthy subjects during eyes open/closed (EO/EC) relaxed rest states, has been analyzed here. Using simple similarity measurements based on correlation and distance measures of the test EEG sequences from the template vectors, an average recognition rate of up to 90 % is achieved, by combining spectral features from delta and alpha bands extracted from selected 19 EEG channels. Experimental results explicitly show the usefulness of combining subject-specific alpha and delta bands in future biometric recognition systems. Further investigation is essential to precisely analyze the system and to improve recognition accuracy.

Electroencephalography (EEG) signal processing to decode motor imagery (MI) involves high-dimensional features, which increases the computational complexity. To reduce this computational burden due to the large number of channels, an iterative multiobjective optimization for channel selection (IMOCS) is proposed in this paper. For a given MI classification task, the proposed method initializes a reference candidate solution and subsequently finds a set of the most relevant channels in an iterative manner by exploiting both the anatomical and functional relevance of EEG channels. The proposed approach is evaluated on the Wadsworth dataset for the right fist versus left fist MI tasks, while considering the cross-validation accuracy as the performance evaluation criteria. Furthermore, 12 other dimension reduction and channel selection algorithms are used for benchmarking. The proposed approach (IMOCS) achieved an average classification accuracy of about 80% when evaluated using 35 best-performing subjects. One-way analysis of variance revealed the statistical significance of the proposed approach with at least 7% improvement over other benchmarking algorithms. Furthermore, a cross-subject generalization of channel selection on untrained subjects shows that the subject-independent channels perform as good as using all channels achieving an average classification accuracy of 61%. These results are promising for the online brain-computer interface (BCI) paradigm that requires low computational complexity and also for reducing the preparation time while conducting multiple session BCI experiments for a larger pool of subjects.

All-pass transformation (APT)-based variable digital filters (VDFs), also known as frequency warped VDFs, are typically used in various audio signal-processing applications. In an APT-based VDF, all-pass filter structures of appropriate order are used to replace the delay elements in a prototype filter structure. The resultant filter can provide variable frequency responses with unabridged control over cutoff frequencies on the fly, without updating the filter coefficients. In this brief, we briefly review the first- and second-order APT-based VDFs along with their hardware implementation architectures, and provide generalized design procedures to realize them as per required specifications. We also propose the modified pipelined hardware implementation architectures for both the first- and second-order APT-based VDFs. Field-programmable gate array implementation results of different first- and second-order APT-based VDF designs for both nonpipelined and pipelined implementation architectures are presented. An analysis of the results shows that the proposed pipelined implementation architectures result in high-speed VDFs, achieving high operating frequencies that are independent of the prototype filter order, for both the first- and second-order APT-based VDF designs.

The frequency transformation based filters (FT filters) provide an absolute control over the cutoff frequency. However, the cutoff frequency range (Ωc_range) of the FT filters is limited. The second-order frequency transformations combined with coefficient decimation technique based filter (FTCDM filter) has wider Ωc_rangecompared with the FT filter; however, the ratio of transition bandwidth of the transformed filter to that of the prototype filter, tbwFT/tbwmod, is large over a significant portion of Ωc_range. In this paper, we propose a novel idea of relaxing the one-to-one mapping condition between the frequency variables, to overcome the issue of limited Ωc_rangefor tbwFT≤ tbwmod. In the proposed modified second-order frequency transformation based filter (MSFT filter), we relax the one-to-one mapping condition between the frequency variables and use low-pass to high-pass transformation on the prototype filter to achieve wider Ωc_rangewith tbwFT≤ tbwmod. Design example shows that the MSFT filter provides 3 and 1.22 times wider Ωc_rangecompared to FT and FTCDM filters, respectively.

Brain-Computer Interface (BCI) is an alternative communication and control channel between brain and computer which finds applications in neuroprosthetics, brain wave controlled computer games, etc. This paper presents a comparative study on the effects of audio and visual modalities of input stimuli in enhancing the memory and attention level of a person. The proposed game requires the player to memorize a sequence of objects and then correctly select them in the order of their appearance for the different modalities of stimuli, separately. It also enables the player to control the game using attention based brain signals. The attention level of a person is quantified using a modified form of sample entropy feature of Electroencephalogram (EEG). Previous studies have already shown the effectiveness of visual modality for enhancing cognitive skills of person. This paper aims to show the relative effectiveness of the audio stimuli in enhancing the attention and memory of a person.

Electroencephalography (EEG) is the most widely used Brain-Computer Interface (BCI) modality to record brain signal. Unlike other neuroimaging modalities like fMRI and PET, EEG is not very effective in localizing the brain sources. However, with the advent of inverse modeling techniques for source localization, it is possible to use EEG as an alternative neuroimaging technique. In this paper, source localization using EEG signal is used to analyze single-trial movement imagination (MI) tasks. Wadsworth physiobank dataset of 109 subjects performing right hand vs left hand movement imagination is considered. Forward modeling based on 3 layered head geometry is co-registered with ICBM 152 template anatomy, which is a non-linear average of fMRI scans of 152 subjects. Inverse modeling is done with the help of Standardized Low Resolution Electromagnetic Tomography (sLORETA). The proposed method presents some preliminary results on how source localization could be used to identify the moment (time instant) of brain source activation even within a single trial.

The notion of developing thought controlled devices (games, robots, cars etc.) is becoming increasingly popular with the introduction of low cost commercial headsets that record neuroelectric activity and the extensive research in the area of Brain Computer Interfaces (BCIs). In this paper, we study the feasibility of using a commercial low cost EEG amplifier which has only limited number of electrodes, to develop a motor control BCI system. The objective is to extract brain activity responsible for direction specific imagined and executed motor activity, which can be used to identify the motor task performed by the user using the simultaneously recorded EEG. An experiment is conducted to engage the user in bi-directional horizontal movement execution and imagination of the dominant hand. The analysis includes investigation of the time-frequency bins of the recorded EEG that provides maximum discrimination of directional movement. Further, the features are extracted using Filter Bank Common Spatial Pattern (FBCSP), followed by Fisher Linear Discriminant (FLD) for classification. The classification performance at various time instants of each trial are considered, and a control strategy was introduced at the classifier output to enhance performance. The performance in terms of average classification accuracy over five subjects is obtained as 81.3 % (movement execution) and 82.4 % (movement imagination). The results indicate the applicability of this EEG-BCI system to provide directional motor control to an interfaced device such as a robotic arm or a game element.

Electroencephalography (EEG) signals have widely been used for developing Brain Computer Interface (BCI) systems. BCI systems generally record, process and extract informative features hidden in brain signals, ultimately aiming towards "human thought translation". A number of EEG based BCI studies focus on estimation and enhancement of cognitive functions such as attention, memory and creativity, assessing mental workload, fatigue, etc. Detection of discriminative EEG features associated with presentation of familiar and non-familiar images is not well-studied so far, though it is worthy to explore its usability even in EEG-based authentication systems. In this paper, a set of time-frequency based EEG features are investigated, while the subjects are exposed to familiar and unfamiliar visual stimuli (images) for a fixed time period during the experimental paradigm. The results show that combination of features such as band power values, signal peaks, activity and mobility of the signal gives an average accuracy of 70.71% in classifying between familiar and unfamiliar images among 7 subjects. Further investigation is necessary to improve the classification performance and to reduce the effects of intersubject and intra-subject variability of EEG signals during feature extraction.

The next-generation heterogeneous networks are expected to offer higher data-rate and better QoS to the customers by leveraging smaller cells like femtocells and making use of orthogonal frequency division multiple access (OFDMA). However, uncoordinated dense deployment of femtocells in a macrocell network pose unique challenges involving cross-tier interference and resource management which may lead to significant performance degradation in the system. As part of addressing these challenges, this paper proposes a Cross- Polarized Complementary Frequency Allocation (CPCFA) strategy which exploits frequency and polarization diversity to mitigate interference in two-tier femto-macro networks. This strategy combines the benefits of reverse frequency allocation (RFA) and orthogonal polarized transmission which is analyzed as a potential solution for maximizing spectral efficiency and minimizing interference in heterogeneous networks. The results of analytic and simulation studies prove that CPCFA increases the scope for an easily implementable, remarkable opportunity in the context of two-tier femto-macro network that can substantially increase the system capacity, area of interference-free femtocell exclusive region as well as the coverage probability without additional network complexities.

Multistandard radios typically employ digital filter banks (FBs) for channelization of wideband input signals consisting of multiple radio channels corresponding to different wireless communication standards. In this paper, we propose a new design technique to obtain uniform as well as nonuniform digital FBs for multistandard channelization. In the proposed FB design technique, the improved coefficient decimation method (ICDM) is used to obtain different low-pass, high-pass, and multiband frequency responses using a single low-pass prototype filter. These frequency responses are algebraically operated upon using appropriate spectral subtraction, complementary filter response operation, and frequency response masking operations to obtain the desired sub-bands in the FB. With the help of suitable design examples, we show that our FB is a low complexity and flexible alternative to the other digital FBs in the literature, in both uniform and nonuniform channelization scenarios. The proposed ICDM-based FB is shown to be able to achieve a higher number of distinctly located sub-bands with twice the center frequency resolution when compared with the discrete Fourier transform-based FB (DFTFB) and the conventional coefficient decimation method-based FB (CDFB). For a uniform channelization design example for wireless communications, implementation results show that the proposed ICDM-based FB achieves 70.92% and 15.49% reductions in resource utilizations and 58.72% and 23.13% reductions in power consumptions when compared with the DFTFB and CDFB respectively. Also, for a nonuniform channelization design example for wireless communications, the proposed ICDM-based FB shows an 11.46% reduction in multiplication complexity when compared with the CDFB.

This paper presents a design of linear-phase, low-complexity, reconfigurable digital filter bank that offers independent and complete control over the bandwidth as well as the center frequency of all subbands. The proposed filter bank is designed by integrating spectral parameter approximation (SPA) technique with the modified coefficient decimation method (MCDM), referred to as SPA-MCDM-FB. The architectural details, design examples and complexity comparisons show that the SPA-MCDM-FB is easy to design and offers substantial savings in gate count, number of variable multipliers and group delay over other filter banks. Moreover, these savings increase further with the increase in the filter-bank resolution (i.e., number of subbands). The SPA-MCDM-FB is then combined with the upper confidence bound (UCB)-based decision-making algorithm to search the vacant band(s) of any desired bandwidth for spectrum-sensing application in cognitive radio (CR). The simulations results verify that the proposed scheme offers superior performance [i.e., improved utilization of vacant subband(s)] and needs fewer gate counts compared to uniform filter bank and UCB-algorithm-based schemes. Furthermore, the functionality and advantages of the SPA-MCDM-FB are also verified for the channelization operation in CR supporting multiple communication standards.

The neuro engineering research over the past decades has established Electroencephalography based Brain Computer Interface (EEG-BCI) systems as an efficient means of decoding brain activity. Motor control BCI is a category of BCI that analyzes neural activity recorded over sensory motor area to classify or decode intended motor tasks. For a BCI system, it is desired to have defined and independent output control commands. Decoding movement trajectory parameters such as instantaneous position, speed coordinates from non-invasive brain recordings can thus be a key contribution in motor control BCI applications. In this study, we use Multiple Linear Regressor to estimate the hand movement trajectory from spectrally localized multi-sensor EEG. The algorithm is validated using data collected from subjects as they perform 2-dimensional center-out hand movement towards pre-defined targets at varying speeds. The spatio-temporal variations in motor activity based neural activation patterns using metrics derived from MLR estimator is investigated. The contribution of the predictors to the regression equation and decoding performance at various stages of movement are also studied. An average correlation of 0.63 (p<;0.005) between recorded and estimated trajectory is obtained using the method. The temporally varying involvement of motor, pre-motor and parietal areas; movement task dependent activations and time-varying sensor contribution in reconstruction are further demonstrated.

Brain-Computer Interface (BCI) technique is considered as an efficient alternative modality for improving brain functions such as attention and cognition, based on real time feedback of Electroencephalogram (EEG) signals and their self-regulation. Commercialization of EEG headsets provides tremendous opportunities and possibilities for this technology to employ EEG in video games for cognitive-skill enhancement. This paper proposes a multi-player video game in 3-D environment controlled by EEG features related to 3 different levels of attention. A number of conventional control mechanisms present in commercial games such as keyboard strokes have also been integrated in the game. Three different levels of attention have been detected from players based on their sample entropy features and band power values in alpha, beta and theta bands of EEG. Three subjects have successfully navigated in the designed 3-D environment using EEG based controls as well as keyboard inputs. Experimental results reveal the feasibility of integrating brain signal based inputs along with conventional control inputs in the context of multi-player neurofeedback games for improving brain functions.

Brain computer interface (BCI) based neurofeedback games have the potential to enhance the cognitive skills of healthy people as well as subjects with cognitive and memory impairment. Electroencephalogram (EEG) has been used as a common brain imaging modality as it is easy and cheap among all the other non-invasive techniques. This paper proposes an EEG driven gaming interface where the subject's attention (concentration) is used to control the game successfully. The proposed game scenario requires the player to push a ball from one end of game's graphical user interface to the other end using his attention level. Attention level of the player is quantified using the ratio of theta to beta band power in EEG signals. The experimental analysis shows that the proposed game is capable of enhancing player's attention skill as well as enhances the ability of the player to sustain attention for longer duration. This “ball game” can be effectively used in neurofeedback training for attention deficit children.

Brain-Computer Interface (BCI) provides a direct communication pathway between brain and computer/machine bypassing the conventional pathway of nerves and muscles. Electroencephalography (EEG) is the most commonly used brain signal acquisition technique in BCI systems. The use of motor imagery (MI) patterns in EEG-based BCI has been proven as an effective method to translate the user's movement intention to commands for controlling external devices. To obtain high classification accuracy of MI, conventional EEG based BCI employ a large number of scalp electrodes. However, this is inconvenient in the clinical scenarios where preparation time is of paramount importance. This paper proposes a channel selection method which utilizes a priori information of the MI task and iteratively optimizes the number of relevant channels, thereby improving the classification accuracy. The proposed method is employed in BCI Competition III dataset IVa and BCI Competition IV, dataset 2a to classify hand and foot MI tasks. The proposed method results in better accuracy than state-of the- art methods with a significant reduction in the number of channels. © 2014 IEEE.

Brain-Computer Interface (BCI) is an emerging modality for direct communication between brain and computer, bypassing brain's conventional communication pathway of the nerves and muscles. Though BCI investigations have been targeting on the development of assistive devices for paralyzed patients initially, recent BCI research exploits the possibilities of BCI in entertainment and cognitive-skill enhancement through neurofeedback games also. Neurofeedback is an effective tool for boosting cognitive skills of both healthy and attention-deficit people based on real-time feedback and self-regulation of brain signals. This paper investigates the feasibility of employing EEG features related to sustained attention and overt visual attention-shift towards left or right visual periphery from a fixation point in the context of a neurofeedback game. Three healthy subjects have successfully played the proposed neurofeedback game by selecting the targets solely by EEG features related to overt visual attention, offering an average accuracy of 72.22%.

This brief presents a new low-complexity reconfigurable fast filter bank (RFFB) for wireless communication applications such as spectrum sensing and channelization. In RFFB, the bandwidth and center frequency of sub-bands can be varied with high frequency resolution without hardware reimplementation. This is achieved with an improved modified frequency transformation-based variable digital filter (MFT-VDF) at the first stage of the proposed multistage implementation. Existing second-order frequency transformation-based low-pass VDFs have limited cutoff frequency range which is approximately 12.5% of the sampling frequency. The proposed low-pass MFT-VDF offers unabridged control over the cutoff frequency on a wide frequency range thereby, improving the cutoff frequency range of existing VDFs. The design example shows that the RFFB is easy to design and offers substantial savings in gate counts over other filter banks.

The channel filters in multi-standard wireless communication receivers must be capable of extracting radio channels (frequency bands) of distinct bandwidths located at different center frequencies. In this paper we propose a method to realize low complexity reconfigurable channel filters based on decimation, interpolation and masking techniques. We show that our method provides approximately twice the number of channel filter frequency responses compared to the existing reconfigurable channel filter realization method. The resolution of center frequency locations of our channel filter responses is also twice that of the existing method. The enhancement in center frequency resolution and the increased number of channel filter frequency responses are obtained without any hardware overhead.

Decoding hand movement parameters (for example movement trajectory, speed etc.) from scalp recordings such as Electroencephalography (EEG) is a challenging and less explored area of research in the field of Brain Computer Interface (BCI) systems. By identifying neural features underlying movement parameters, a detailed and well defined control command set can be provided to the BCI output device. A continuous control to the output device is better suited for practical BCI systems, and can be achieved by continuous reconstruction of movement trajectory than discrete brain activity classifications. In this study, we attempt to reconstruct/estimate various parameters of hand movement trajectory from multi channel EEG recordings. The data for analysis is collected by performing an experiment that involved centre-out right hand movement tasks in four different directions at two different speeds in random order. Multiple linear regression (MLR) strategy that fits the recorded movement parameters to a set of spatial, spectral and temporal localized neural data set is adopted. We propose a method to define the predictor set for MLR, using wavelet analysis, to decompose the signal into various sub bands. The correlation between recorded and estimated parameters are calculated and an average correlation coefficient of (0.56 ± 0.16) is obtained over estimating six movement parameters. The promising results achieved using the proposed algorithm, which are better than that of the existing algorithms, indicate the applicability of EEG for continuous motor control.

In this brief, an efficient implementation of reconfigurable warped digital filter with variable low-pass, high-pass, bandpass, and bandstop responses is presented. The warped filters, obtained by replacing each unit delay of a digital filter with an all-pass filter, are widely used for various audio processing applications. However, warped filters require first-order all-pass transformation to obtain variable low-pass or high-pass responses, and second-order all-pass transformation to obtain variable bandpass or bandstop responses. To overcome this drawback, the proposed method combines the warped filters with the coefficient decimation technique. The proposed architecture provides variable low-pass or high-pass responses with fine control over cut-off frequency and variable bandwidth bandpass or bandstop responses at an arbitrary center frequency without updating the filter coefficients or filter structure. The design example shows that the proposed variable digital filter is simple to design and offers substantial savings in gate counts and power consumption over other approaches.

The decoding of hand movement kinematics using non-invasive data acquisition techniques is a recent area of research in Brain Computer Interface (BCI). In this work, we use an Electroencephalography (EEG) based BCI to decode directional information from the brain data collected during an actual hand movement experiment. The objective is to find the discriminative features of movement related potential that can classify any two directions out of the four orthogonal directions in which subject performs right hand movement. The performance using Wavelet-Common Spatial Pattern (W-CSP) algorithm and its variations in terms of spatial regularization is studied and compared. The work further analyzes the involvement of frontal, parietal and motor regions in carrying movement kinematics information with the help of spatial plots given by CSP. The performance variability for different directions in various subjects is another important observation in our results. The work aims to provide a more refined movement control command set for BCIs by developing efficient techniques to decode the direction of movement. © 2012 IEEE.

This paper presents the design of a variable linear phase finite impulse response filter based on second order frequency transformations and coefficient decimation. The design of variable digital filters (VDFs) using first and second order frequency transformations have been proposed in literature. The VDF using second order transformation has better cut-off slope characteristics compared to the VDF using first order transformation. However, the former has the drawback of limited range (approximately 25% of the half of the sampling frequency) over which the cut-off frequency, fc, can be varied. It also fails to provide variable lowpass, highpass, bandpass or bandstop responses from a fixed-coefficient lowpass filter using the same architecture. The architecture proposed here overcomes the above mentioned disadvantages using coefficient decimation technique. The design example shows that the range over which fccan be varied is 2.65 times wider in the proposed VDF than the VDF in [7] and for a given frequency range, the proposed VDF offers a total gate count saving of 33% and 41% over the VDF in [11] and [7] respectively. Also, the proposed architecture provides variable lowpass, highpass, bandpass or bandstop responses from a fixed coefficient lowpass filter.

An enormous influx of wireless services and devices coupled with inefficient usage of electromagnetic spectrum has led to an apparent scarcity of usable radio bandwidth. Cognitive radio is leading the trend for increasing the spectrum efficiency by utilising the vacancy in the radio spectrum created by absence of the licensed primary user. This paradigm shift can only take place if the means to detect the primary user are well established so that an ecosystem can be created where both primary and secondary users can co-exist without interfering with each other. In this study the authors propose a two-stage detection mechanism which gives an improved performance over conventional single-stage detectors yet optimises the usage of the second stage, thereby reducing the sensing time as compared to conventional two-stage spectrum sensing algorithms. A hardware implementation of the algorithm has also been done to quantify the area and power consumption values. By utilising the second-stage optimally, the algorithm presented in this study helps in reducing the sensing time by 86% as compared with the conventional two-stage detector. By not activating the second stage at high SNRs, the proposed algorithm saves 0.915 W of dynamic power out of a total of 1.09 W, thus effectively reducing the dynamic power consumption by 84%.

In this paper, we propose a multi-resolution filter bank (MRFB)-based on the fast filter bank design for multiple resolution spectrum sensing in military radio receivers. The proposed method overcomes the constraint of fixed sensing resolution in spectrum sensors based on conventional discrete Fourier transform filter banks (DFTFB). The flexibility in realizing multiple sensing resolution spectrum sensor is achieved by suitably designing the prototype filter and efficiently selecting the varying resolution subbands without hardware re-implementation. Design examples show that the sensing performance of proposed MRFB is comparable to that of conventional fixed resolution DFTFB. The complexity comparison shows that the proposed MRFB architecture has a gate count reduction of 36.5% over the DFTFB. The proposed MRFB architecture achieves an average power reduction of 20.8% over DFTFB.

This paper presents a new method for the design of finite impulse response (FIR) filter that provides variable frequency responses. The proposed idea is to replace each unit delay operator in a fixed-coefficient FIR filter with the 2ndorder FIR fractional delay (FD) structure and the cutoff frequency, fcof the filter is changed by changing the FD value. The change in FD results in change in amplitude and length of an impulse response. This in turn changes fcand transition bandwidth (TBW) of an FIR filter. The mathematical relation between cut-off frequency, TBW and FD value D is derived. The design example shows that the proposed method provides very fine control over fc.

Spectrum sensing techniques for Cognitive radios have led to the emergence of a wide variety of analytical methods to detect the presence of a primary user. Though each technique has its own advantages, the disadvantages associated with them makes a standalone implementation of the technique unviable for practical use. A two stage detector for spectrum sensing can be used to mitigate the disadvantages of a single stage detection technique and synergize the advantages offered by the individual methods. However, a two stage analysis increases the time taken to sense the spectrum and arrive at a conclusive result. In this paper, we propose an algorithm which can be used to minimize the time taken by a two stage detector. Simulation results have been used to show that the proposed algorithm leads to a large savings in time as compared to an existing two stage detection algorithm. The savings in time increases as spectrum utilization of the band under consideration becomes more sparse.

Computationally intensive functions in the radio baseband have been typically implemented using dedicated hardware accelerators. The requirement of flexibility and multimode support in emerging communication paradigms has introduced new design challenges for implementing these accelerator cores, which were typically optimized for a single mode of operation. This paper focuses on the design of multimode accelerators for the channelization function in a flexible radio. This work introduces a theoretical framework, to systematically identify commonalities and redundancies in the channelization specification across multiple modes. A novel sample rate conversion ratio factorization strategy is also introduced, that allows a significant portion of the channelization accelerator to be hardwired and reused across multiple modes of operation.

The performance of a Brain-Computer Interface (BCI) depends on reliable feature extraction and accurate classification. Motor imagery has been successfully used in BCI for communication and control. During motor imagery, for EEG based BCI, it was known that the discriminative frequency bands are subject-specific. Moreover, such discriminative frequency bands for each subject might vary from time to time. In this paper, we investigate the variability of discriminative spectral ranges and its impact on classification accuracy. It is found that for each subject, his discriminative frequency bands changes significantly from session to session, but keeps almost stable within a session. We then propose a method to adaptively update the discriminative frequency bands using Time-Frequency fisher ratio. From the experimental analysis, it is found that we can reduce the average error rate by 11.50% compared to the case where fixed discriminative frequency bands obtained from calibration session are used.

The growing energy footprint and environmental costs of information and communication technologies has created an awareness of the need for greener communications. However, the task of reducing the energy footprint of wireless infrastructure and terminals is daunting due to the requirements of flexibility and reconfigurability in emerging paradigms like 4G. This paper addresses the flexibility and power consumption challenges of channel filtering, which is one of the most computationally intensive kernels in the radio baseband. Power reduction strategies for programmable time-shared filters have been generally focused on the dynamic power, which has been replaced by leakage power as the dominant mode of power consumption in nanoscale CMOS devices. We investigate the role of parallelism in reducing the nanoscale CMOS power consumption. We also propose a class of programmable time-shared filters that are more area efficient than traditional folded direct form filters, when the level of parallelism is increased.

Reconfigurability and low complexity are the two key requirements of finite impulse response (FIR) filters employed in multistandard wireless communication systems. In this paper, two new reconfigurable architectures of low complexity FIR filters are proposed, namely constant shifts method and programmable shifts method. The proposed FIR filter architecture is capable of operating for different wordlength filter coefficients without any overhead in the hardware circuitry. We show that dynamically reconfigurable filters can be efficiently implemented by using common subexpression elimination algorithms. The proposed architectures have been implemented and tested on Virtex 2v3000ff1152-4 field-programmable gate array and synthesized on 0.18 ¿m complementary metal-oxide-semiconductor technology with a precision of 16 bits. Design examples show that the proposed architectures offer good area and power reductions and speed improvement compared to the best existing reconfigurable FIR filter implementations in the literature.

A new approach to implement computationally efficient reconfigurable filter banks for multi-standard wireless receivers is presented in this paper. Based on the concepts of tree-structured quadrature mirror filter bank (TQMFB) and coefficient decimation approach, a reconfigurable and efficient tree-structured non-uniform filter bank (TNFB) is proposed in this paper. The proposed filter bank is designed to extract channels of non-uniform bandwidths with reduced complexity when compared to TQMFB. Each stage of the proposed filter bank consists of a modal filter and a complementary delay to obtain the low-pass and high-pass channels respectively. Design examples show that the proposed TNFB offers an average multiplication rate reduction of 69% over the TQMFB.

Brain computer interface (BCI) provides a direct communication pathway between a human and an external device. In this paper, we propose a new dasiadiscriminative filterbank common spatial pattern (DFBCSP)psila algorithm to select the subject-specific filters automatically during training for a motor imagery based BCI. The subject-specific filters are selected using the fisher ratio values of filtered electroencephalogram (EEG) signal. The channel dasiaC3psila alone could give sufficient information to select the discriminative filterbank for the proposed system. We have also explored the possibility of boosting the system performance by including dynamic temporal features. Fusion of static and dynamic features in the proposed DFBCSP frame work gave an average test accuracy of 92.44%, which is significantly better than conventional filterbank based common spatial pattern algorithms.

A new approach to implement computationally efficient reconfigurable finite impulse response (FIR) filter is presented in this paper. If the coefficients of an FIR filter are decimated by M, i.e., if every Mthcoefficient of the filter is kept unchanged and remaining coefficients are changed to zeros, a multi-band frequency response will be obtained. The resulting frequency responses will have centre frequencies at 2pik/M, where k is an integer ranging from 0 to M-1. If these multi-band frequency responses are selectively masked using inherently low complex wide transition-band masking filters, different low-pass, high-pass, bandpass, and bandstop filters can be obtained. If every Mthcoefficient is grouped together removing the zero coefficients in between, a decimated frequency response in comparison to the original frequency response is obtained. In this paper, we also show the design of a reconfigurable filter bank using the above approach.

The most computationally demanding part in the digital front-end of a Software radio receiver is the channelizer, which operates at the highest sampling rate. The channelizer extracts multiple narrowband channels from the digitized wideband input signal. The limitation of the conventional uniform Discrete Fourier transform (DFT) filter bank channelizer is that, it is incapable of extracting channels of multiple bandwidths, as the prototype filter has fixed equal bandwidths. Reconfigurable filter bank architecture for the SDR channelizer, based on multi-stage frequency response masking technique is proposed in this paper. The proposed architecture is capable of extracting channels with different bandwidths corresponding to different wireless communication standards. Design examples show that proposed architecture offers a complexity reduction of 97.2 % over the conventional Per-Channel (PC) approach and DFT filter banks.

This paper investigates the design of very low complexity multiplierless linear phase FIR filters for use in the channelizer of multi-standard software defined radios. A technique for reducing the hardware complexity of linear phase FIR digital filters which minimizes the adder depth and the number of adders in the multiplier block is introduced and is used to implement a multistage, multi-standard decimating filter. The design reuses components for different communications standards and is thus ideal for use in systems which support dynamic reconfiguration.

Efficient implementation of the sample rate converter is extremely important to reduce the power consumption of software defined radios. This becomes even more important when sigma delta converters are used for the analog to digital conversion due to the very high oversampling rates involved. In a multistandard radio, the sample rate converter should also be capable of handling variable conversion ratios and channel bandwidths. In this paper we propose a simple scheme to efficiently factorize any large arbitrary factor into integral and fractional factors. We also suggest efficient reconfigurable hardware architectures to implement these conversion factors.

Under a recent vision, cognitive radio (CR) is an adaptive spectrum sharing paradigm targeted to provide opportunistic spectrum access to secondary users for whom the frequency bands have not been licensed. The key tasks in a CR are to sense the spectral environment over a wide frequency band and allow secondary users (CR users) to dynamically transmit/receive data over frequency bands unutilized by licensed primary users. Thus the CR transceiver should dynamically adapt its channel in response to the time-varying frequencies of wideband signal for seamless communication. In this paper, we present a low complexity reconfigurable filter architecture based on multi-band filtering and frequency masking techniques for dynamic channel adaptation in CR terminal. The proposed multi-standard architecture is capable of adapting to channels having different bandwidths corresponding to the channel spacing of time varying channels. Design examples show that proposed architecture offers 12.2% power reduction and 26.5% average gate count reduction over conventional per-channel based architecture.

The complexity of linear-phase finite-impulse-response (FIR) filters is dominated by the complexity of coefficient multipliers. The number of adders (subtractors) used to implement the multipliers determines the complexity of the FIR filters. It is well known that common subexpression elimination (CSE) methods based on canonical signed digit (CSD) coefficients reduce the number of adders required in the multipliers of FIR filters. A new CSE algorithm using binary representation of coefficients for the implementation of higher order FIR filters with a fewer number of adders than CSD-based CSE methods is presented in this paper. We show that the CSE method is more efficient in reducing the number of adders needed to realize the multipliers when the filter coefficients are represented in the binary form. Our observation is that the number of unpaired bits (bits that do not form CSs) is considerably few for binary coefficients compared to CSD coefficients, particularly for higher order FIR filters. As a result, the proposed binary-coefficient-based CSE method offers good reduction in the number of adders in realizing higher order filters. The reduction of adders is achieved without much increase in critical path length of filter coefficient multipliers. Design examples of FIR filters show that our method offers an average adder reduction of 18% over the best known CSE method, without any increase in the logic depth.

This paper is focus on to design a sampling system with minimum sampling rate for signals in the shift invariant Hilbert space. To achieve this goal, we propose a method to calculate the rate of innovation (RI) of signals in the Hilbert space. The RI is then used to identify a suitable sampling kernel as well as the sampling rate for a specific signal. We show that the RI of the kernel should be greater or equal to the RI of the signal for the signal to be perfectly reconstructible. The minimum sampling rate depends on the RI of the signal. Examples are included to demonstrate how our method is applied to calculate the RI. Some known sampling theories can also be fitted into the framework of sampling system with minimum sampling rate.

The most computationally demanding block in the digital front end of a software defined radio (SDR) receiver is the channelizer which operates at the highest sampling rate. Reconfigurability and low complexity are the two key requirements of the SDR channelizers. An architecture for implementing low complexity and reconfigurable finite impulse response (FIR) filters for channelizers is proposed in this paper. Our method is based on the binary common subexpression elimination (BCSE) algorithm. The proposed architecture guarantees minimum number of additions at the adder level and also at the full adder (FA) level for realizing each adder needed to implement the coefficient multipliers. The proposed architecture has been synthesized on 0.18μm CMOS technology. The synthesis results show that the proposed reconfigurable FIR filter can operate at high speed consuming minimum area and power. The average reductions in area and power are found to be 49% and 46% respectively with an average increase in speed of operation of 35% compared to other reconfigurable FIR filter architectures in literature.

The most computationally demanding block in the digital front end of a software defined radio (SDR) receiver is the channelizer which operates at the highest sampling rate. Finite impulse response (FIR) filters are employed as channel filters in SDR receivers. These channel filters must be less complex and reconfigurable. A new reconfigurable architecture for the implementation of channel filters based on frequency response masking (FRM) technique is proposed in this paper. Our architecture offers multiple levels of reconfigurability in addition to the inherent low complexity features offered by the FRM technique. Design results show that our method offers an average adder reduction of 37% over binary subexpression elimination (BSE) technique and 57% over canonical signed digit (CSD) based techniques.

The complexity of finite impulse response (FIR) filters is dominated by the number of adders (subtractors) used to implement the coefficient multipliers. It is well known that common subexpression flimination (CSE) method based on canonic signed digit (CSD) representation considerably reduces the number of adders in coefficient multipliers. Recently, a binary based CSE (BSE) technique was proposed, which produced better reduction of adders compared to the CSD based CSE. In this paper, we propose a new 4-bit Binary based CSE (BCSE) method which employs 4-bit common subexpressions (CSs). Design examples show an average adder reduction of 31.2 % over the conventional CSD based CSE and 15% reduction over BSE.

This paper presents the novel designs of reversible sequential circuits (latches and flip flops). The proposed reversible latches and flip flops are designed from reversible Fredkin, Feynman and Toffoli gates. Two new reversible gates called modified Fredkin gate (MFG) and modified Toffoli gate (MTG) are also proposed to design the optimized implementations. The proposed designs are better than the recently proposed ones in terms of number of reversible gates and garbage outputs. In order to reach towards the goal of transistor implementations of proposed reversible sequential circuits, transistor implementation of the existing Feynman gate, Fredkin gate, Toffoli gates as well as the proposed MTG and MFG are also proposed. The proposed transistor implementations are completely reversible in nature, i.e., suitable for both the forward and backward computation.

Reversible logic is emerging as a promising computing paradigm having its applications in low power VLSI design, quantum computing, nanotechnology and optical computing. In this paper, a new 4 times 4 reversible gate termed `OTG' (online testable gate) is proposed suitable for online testability in reversible logic circuits. OTG can also work singly as a reversible full adder with a bare minimum of two garbage outputs. OTG is shown better than the recently proposed R1 gate (introduced for providing online testability in reversible logic circuits), in terms of computation complexity. The proposed reversible gate is combined with the existing 4 times 4 Feynman gate to design online testable reversible adders such as ripple carry adder, carry skip adder and BCD adder. The efficient reversible design of two pair rail checker is also shown in this paper. The testable reversible circuits proposed in this work are shown to be better than the recently proposed testable designs in terms of number of reversible gates, garbage outputs and unit delay

The complexity of finite impulse response (FIR) filters is dominated by the number of adders (subtractors) used to implement the coefficient multipliers. A greedy common subexpression elimination (CSE) algorithm with a look-ahead method based on the canonic signed digit (CSD) representation of filter coefficients for implementing low complexity FIR filters is proposed in this paper. Our look-ahead algorithm chooses the maximum number of frequently occurring common subexpressions and hence reduces the number of adders required to implement the filter. This adder reduction is achieved without any increase in critical path length. Design examples of FIR filters show that the proposed method offers an average adder reduction of about 20% over the best known CSE method.

This paper extols the virtues of information theoretic approach to the synthesis of reduced multirooted directed acyclic graph (DAG) representation for the multiplier block of FIR filters. The proposed maximum likelihood decomposition algorithm can be viewed as an efficient divide-and-conquer approach with dynamic tracking of the statistic of weight-two subexpressions. As isomorphic subgraphs of the resultant reduced multirooted binary partition tree (MBPT) represent common subexpressions, higher weight common subexpressions are eliminated implicitly in the graph synthesis process. Experimental results show that the proposed algorithm produce designs with good tradeoffs for low logic complexity and logic depth

The complexity of linear phase finite impulse response (FIR) filters used in the channelizer of a software defined radio (SDR) receiver is dominated by the complexity of coefficient multipliers. It is well known that common subexpression elimination (CSE) methods based on canonical signed digit (CSD) coefficients produce low complexity FIR filter coefficient multipliers. A new CSE algorithm based on the binary representation of filter coefficients is presented in the paper. Design examples of channel filters employed in the digital advanced mobile phone systems (D-AMPS) and personal digital cellular (PDC) receivers show that the proposed method offers an average adder reduction of 23% over the conventional CSD-based CSE method

This paper and its companion paper (entitled Part II - algorithm) together present techniques for low power realization of finite impulse response (FIR) filters using improved differential coefficients method (DCM). This paper presents the necessary foundation and terminology of the DCM. The companion paper describes our algorithm and presents design examples. In contrast to the conventional DCM that is formulated at the algorithm-level, our method is formulated at the architecture-level using dedicated shift-and-add-based coefficient multipliers in order to achieve considerable hardware reduction. By employing a differential coefficient-partitioning algorithm (DCPA), we show that the number of full adders and the net memory needed to implement the coefficient multipliers can be significantly reduced. The proposed method is combined with common subexpression elimination method for further reduction of complexity. Experimental results show the average reductions of full adder, memory and energy dissipated achieved by our method over the DCM are 40%, 35% and 50% respectively.

The most computationally demanding block of a software defined radio (SDR) receiver is the channelizer which operates at the highest sampling rate. Reconfigurability and low complexity are the two key requirements of the SDR channelizers. Two new reconfigurable architectures of low complexity finite impulse response (FIR) filters for channelizers are proposed in this paper. Our methods are based on the binary common subexpression elimination (BCSE) algorithm. The proposed architectures are capable of operating at a high speed clock frequency of 109.7 MHz based on Xilinx's Virtex II 2v2000ff896-6 FPGA for a 12-bit FIR filter coefficient. Design examples show that our method offers an average reduction of 23% in the number of addition operations compared to the conventional FIR filter implementations

The most computationally intensive part of the wideband receiver of a software defined radio (SDR) is the channelizer since it operates at the highest sampling rate. Higher order FIR channel filters are needed in the channelizer to meet the stringent adjacent channel attenuation specifications of wireless communications standards. In this paper, we present a coefficient-partitioning algorithm for realizing low power and high-speed channel filters. Design examples of the channel filters employed in the digital advanced mobile phone system (D-AMPS) and personal digital cellular (PDC) receivers show that the average reductions of memory and power consumption achieved using our method over existing method are 25% and 50% respectively

The most computationally intensive part of the wideband receiver of a software defined radio (SDR) is the intermediate frequency (IF) processing block. Digital filtering is the main task in IF processing. The computational complexity of finite impulse response (FIR) filters used in the IF processing block is dominated by the number of adders (subtracters) employed in the multipliers. This paper presents a method to implement FIR filters for SDR receivers using minimum number of adders. We use an arithmetic scheme, known as pseudo floating-point (PFP) representation to encode the filter coefficients. By employing a span reduction technique, we show that the filter coefficients can be coded using considerably fewer bits than conventional 24-bit and 16-bit fixed-point filters. Simulation results show that the magnitude responses of the filters coded in PFP meet the attenuation requirements of wireless communication standard specifications. The proposed method offers average reductions of 40% in the number of adders and 80% in the number of full adders needed for the coefficient multipliers over conventional FIR filter implementation methods

The most computationally intensive part of a wideband receiver is the channelizer. The computational complexity of linear phase finite impulse response (LPFIR) filters employed in the channelizer is dominated by the number of adders used in the implementation of the multipliers. In this paper, two methods are proposed to efficiently implement the channel filters in a wideband receiver based on common subexpression elimination (CSE). We exploit the fact that a significant amount of redundant multiplications exist in the filter-bank channelizer as it extracts multiple narrowband channels from the wideband signal. By forming three and four nonzero-bit super-subexpressions utilizing redundant identical shifts that exist between a two- nonzero-bit common subexpression (CS) and a third nonzero bit, or between two nonzero-bit CS, the number of adders to implement the channel filters can be reduced considerably. Furthermore, the complexity of the adders is analyzed and design examples of the channel filters employed in the digital advanced mobile phone system (D-AMPS) and the personal digital cellular (PDC) channelizers show that the proposed methods offer considerable reduction in the number of full adders when compared to conventional CSE methods.

Algorithms that minimize the complexity of multiplication in digital filters focus on reducing the number of adders needed to implement the coefficient multipliers. Previous works have not analyzed the complexity of each adder, which is significant in low-complexity implementation. A multiplication algorithm for low-complexity implementation of digital filters with a minimum number of full adders (NFAs) and improved speed is proposed here. The authors exploit the fact that when multiplication is implemented using shifts and adds, the adder width can be minimized by limiting the shifts of the operands to shorter lengths. The coefficient-partitioning (CP) algorithm proposed here minimizes the shifts of the operands of the adders by partitioning each coefficient into two subcomponents. The authors show that by combining three methods, the CP algorithm, an efficient coefficient coding scheme known as pseudo floating-point (PFP) representation, and the well-known common subexpression elimination (CSE), the NFAs required in each adder of the multiplier can be reduced considerably. Design examples show that the method offers an average FA reduction of 30% for finite-impulse response (FIR) filters and 20% for infinite-impulse response (IIR) filters over CSE methods.

The computational complexity of linear phase finite impulse response (LPFIR) filters used in the channelizer of a wideband receiver is dominated by the number of adders (subtracters) employed in the multipliers. Common subexpression elimination (CSE) is a well-known technique for minimizing the number of adders in LPFIR filters. An improved CSE method is proposed in this paper, which is used to implement the channel filters of a filter bank channelizer (FBC). In the FBC, each modulated bandpass filter extract one channel from the input wideband signal. The reduction in number of adders is obtained by eliminating redundant multiplications of common subexpressions that exist among the channel filters of the FBC with the input signal. Design example of the channel filters employed in the digital advanced mobile phone system (D-AMPS) show that the proposed method offers considerable reduction in the number of full adders when compared with conventional CSE methods.

The flexibility of a software-defined radio (SRR) depends on its capability to operate in multi-standard wireless communication environments. The most computationally intensive part of wideband receivers is the channelizer, which extracts multiple narrowband signals from adjacent frequency hands. In an SDR receiver, the compatibility of the channelizer with different communication standards is guaranteed by its reconfigurability. This paper presents an efficient channelizer that has a reconfigurable architecture based on quadrature mirror filter bank (QMF) trees. We show that the channelizer can he efficiently implemented using common subexpression based filter structures. An example of dual-mode global system for mobile communication (GSM)/personal digital cellular (PDC) channelizer is discussed to illustrate the proposed design methodology.

It is known that quadrature mirror filters (QMFs) designed using the algorithm in Lim and Yang (1993) suffer from passband anomaly under certain conditions. A modification to this algorithm was proposed in Goh et al. (1996) to overcome this anomaly. However, this design is not a general solution for different stopband edge frequencies. In this paper, we propose a more robust technique to design QMFs, by modifying the frequency domain approach proposed in Lim and Yang with a better initial guess filter obtained by frequency sampling design. The QMF designed employing the proposed technique overcomes the passband anomaly and has excellent frequency response characteristics that are considerably better than those obtained using earlier methods.