A novel 3D deep convolutional neural network (3D-CNN) model called MEGNet3D has been proposed in the paper. MEGNet3D is designed to differentiate between amyotrophic lateral sclerosis (ALS) and healthy individuals from their resting state (eyes open and eyes closed condition) sensor-level magnetoencephalography (MEG) data. The raw MEG data is initially transformed into their time-frequency representation, which are then used as inputs to MEGNet3D. Both magnetometer and gradiometer recordings have been investigated separately. The proposed model exhibits an accuracy of over 75% for most classification conditions. Thus, MEGNet3D is capable of handling high subject variability and shows that spectral-temporal representation of resting-state MEG data yields relevant neural markers related to the existence of ALS. Furthermore, it has also been observed resting state with eyes closed yields better classification accuracy as compared to the resting state with eyes open condition.

Children with dyslexia need specific instructions for spelling and word analysis from an early age. It is important to provide appropriate tools using technology for writing aids to such children that can help them to input text, while providing multiple feedback. However, it is unclear how children with dyslexia can efficiently use a gaze-based virtual keyboard (VK). In this study, we propose to use the typing performance of a multimodal Hindi language eye-gaze-assisted learning system based on a VK to help in the reduction of tracking errors for people with writing and reading deficiencies and to detect children with dyslexia. Performance was assessed at three levels: eye tracker, eye tracker with soft switch, and touchscreen as a baseline modality using a predefined copy-typing task. The system was validated through a series of experiments with 32 children (16 dyslexic and 16 control). The results show that the workload and the usability of the system are substantially different for children with dyslexia. Children with dyslexia have a lower typing performance when using the touchscreen modality or the eye tracker only. The detection of children with dyslexia from others was assessed with seven different types of classifiers using the typing speed on different words (AUC$>$0.9). These results highlight the need to have fully inclusive VKs. This work demonstrates the superior use of a multimodal system with participants having unique neuropsychological conditions and that the proposed system can be used to detect children with dyslexia.

Accurate computational models for clinical decision support systems require clean and reliable data but, in clinical practice, data are often incomplete. Hence, missing data could arise not only from training datasets but also test datasets which could consist of a single undiagnosed case, an individual. This work addresses the problem of extreme missingness in both training and test data by evaluating multiple imputation and classification workflows based on both diagnostic classification accuracy and computational cost. Extreme missingness is defined as having ∼50% of the total data missing in more than half the data features. In particular, we focus on dementia diagnosis due to long time delays, high variability, high attrition rates and lack of practical data imputation strategies in its diagnostic pathway. We identified and replicated the extreme missingness structure of data from a real-world memory clinic on a larger open dataset, with the original complete data acting as ground truth. Overall, we found that computational cost, but not accuracy, varies widely for various imputation and classification approaches. Particularly, we found that iterative imputation on the training dataset combined with a reduced-feature classification model provides the best approach, in terms of speed and accuracy. Taken together, this work has elucidated important factors to be considered when developing a predictive model for a dementia diagnostic support system.

Brain-Computer Interfaces (BCI) permit neural activity to be directly interpreted and used for applications, like therapeutic replacement of lost function (e.g. stroke) or to supplement existing function (e.g. handsfree applications). Two major challenges for BCI are accurate interpretation of neural activity and signal processing speed for real-time applications i.e. correctly decode a user’s intent and the timely execution of that intent. Magnetoencephalography (MEG) has advantages over Electroencephalography (EEG) with respect to spatial and temporal resolution which could potentially allow better decoding of brain activity. High spatial and temporal resolution using MEG generates a large volume of data which must be rapidly preprocessed and classified correctly for practical realtime BCI. This paper presents a simple data processing technique to clean, normalise and reduce data dimensionality, for optimal class label decoding using a simple Logistic Regression classifier. Good decoding performance was achieved using an off-line MEG dataset, with or without data dimensionality reduction, comparable to more complex data pre-processing methods and classifiers already studied.

Generative Adversarial Networks (GAN) have led to important advancements in generation of time-series data in areas like speech processing. This ability of GANs can be very useful for Brain-Computer Interfaces (BCIs) where collecting large number of samples can be expensive and time-consuming. To address this issue, this paper presents a new approach for generating artificial electroencephalography (EEG) data for motor imagery. GANs here use a generator and discriminator networks that consist of Bidirectional Long Short Term Memory neurons. Trained models are evaluated using the dataset 2b from the BCI competition IV. The dataset consists of trials with left and right hand motor imagery. Separate GANs are trained to generate artificial EEG samples corresponding to the two types of trials present in the data set. For the purpose of evaluation, the time-frequency characteristics of the real and artificial EEG signals are compared using Short-Term Fourier Transform and Welch's power spectral density. The results indicate that GANs can capture important characteristics of motor imagery EEG data such as power variations in the beta-band. The power variation in the artificial generated and original signal was in the similar frequency bin when looked at Welch's power spectral density.

Corticomuscular coupling is so far widely explored only for the assessment of motor recovery. However, the possibility of using it for driving hybrid Brain-robot interfaces (h-BRI) is much ignored. Being an important factor for in re-establishing the connection between brain and muscle, corticomuscular coupling might play an important role in providing meaningful neurofeedback of the therapeutic task performance leading to faster recovery. This paper reports a 5-week-long clinical study on 4 chronic stroke patients where we have used correlation of band-limited power time-courses (CBPT), a measure of corticomuscular coupling, as a feature for providing multimodal neurofeedback for hand exoskeleton based therapy. The primary outcome is measured using the standard motor recovery measures of action research arm test (ARAT) and grip-strength (GS). As a secondary measure of recovery, the resting state MEG was also recorded on a weekly basis. The usability of the system was also assessed using the visual-analog-scale measurements of the patients' motivation and fatigue. Results show that the h-BRI performance increased significantly (p-value <; 0.05) from the average 58.16±7.81% from the beginning to the 77.17±3.65% at the end session. A positive change in the ARAT (+23.75) and GS (+9.83 kg) scores was also observed as compared to the baseline. Both of these changes were beyond the minimal clinically important difference (MCID) limit. Overall, the study shows that corticomuscular coupling can be used to design a high-performing BRI which also has the potential of being clinically effective.

Convolutional Neural Network (CNN) has outperformed many traditional linear and polynomial classifiers in various domains. CNN and other deep learning methods have gained attention in the Brain-Computer Interface (BCI) domain also. Here, we investigate a CNN-based model with two different optimizers for reducing error of classification of brain states using EEG motor imagery data. Two different optimizers namely stochastic gradient descent (SGD) and adaptive momentum (Adam) are investigated for increasing classification accuracy using the well-known BCI competition IV 2b dataset. The study was conducted to investigate the feasibility of a single deep learning model for all subjects without compromising on information decoding rate for any of the BCI participants. Using two different models, mean cross-validation accuracy of 80.32% (±2.2) was achieved across participants, which is significantly higher (p<; 0.05) compared to a state-of-the-art deep learning approach.

The performance of a brain–computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier’s generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in BCI systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific “multivariate empirical-mode decomposition” preprocessing technique by taking a fixed band of 8–30[Formula: see text]Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications.

Appropriately combining mental practice (MP) and physical practice (PP) in a poststroke rehabilitation is critical for ensuring a substantially positive rehabilitation outcome. Here, we present a rehabilitation protocol incorporating a separate active PP stage followed by MP stage, using a hand exoskeleton and brain-computer interface (BCI). The PP stage was mediated by a force sensor feedback-based assist-as-needed control strategy, whereas the MP stage provided BCI-based multimodal neurofeedback combining anthropomorphic visual feedback and proprioceptive feedback of the impaired hand extension attempt. A six week long clinical trial was conducted on four hemiparetic stroke patients (screened out of 16) with a left-hand disability. The primary outcome, motor functional recovery, was measured in terms of changes in grip-strength (GS) and action research arm test (ARAT) scores; whereas the secondary outcome, usability of the system was measured in terms of changes in mood, fatigue, and motivation on a visual-analog-scale. A positive rehabilitative outcome was found as the group mean changes from the baseline in the GS and ARAT were +6.38 kg and +5.66 accordingly. The VAS scale measurements also showed betterment in mood ( 1.38), increased motivation (+2.10) and reduced fatigue (0.98) as compared to the baseline. Thus, the proposed neurorehabilitation protocol is found to be promising both in terms of clinical effectiveness and usability.

One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain-computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data-generating mechanism. The objective of this work is, thus, to examine the applicability of the T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: 1) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with intersession as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery, and 2) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis, kernel Fisher discriminant, and support vector machines as well as a conventional type-1 FLS, simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification.

Gait-based person identification suffers from the problem of different covariate factors such as clothing and carrying objects, which drastically reduce the recognition rate. Most existing methods capture dynamic and static information and remove the covariate factors without any systematic study. However, it has been reported in the literature that the head is one of the important features and the removal of the head from static information decreases the recognition rate. In our preliminary study, we developed a novel random walk (RW)-based gait extraction method that retains the head portion and removes certain static body parts to reduce the effect of covariate factors. The RW-based method is a novel gait feature extraction method and should be exploited more for its discriminative power to separate different body parts efficiently. However, the dynamic part is also significant in gait information, which is not very effectively represented in the RW-based gait extraction method. Therefore, a discrete Fourier transform (DFT)-based frequency component of the gait is considered to represent the dynamic part of gait information. Furthermore, we propose a novel gait recognition algorithm that fuses dynamic and static information from DFTand RW-based representations. The proposed method systematically retains the discriminative static gait information along with the frequency attribute embedded as the dynamic gait information. Extensive experiments on the Chinese Academy of Sciences, Institute of Automation and the HumanID datasets have been carried out to demonstrate that the proposed fused gait features-based approach outperforms the existing methods, particularly when there are substantial appearance changes.

Connectivity analysis has become an essential tool for the evaluation of functional brain dynamics. The functional connectivity between different parts of the brain, or between different sensors, is assumed to provide key information for the discrimination of brain responses. In this study, we propose an estimation of effective cortical connectivity measures in frontal and parietal areas of human brain during four different Motor Imagery (MI) tasks. Feedback based brain-computer interface (BCI) technology has been successfully implemented for recovery of stroke patients as it can enhance the neural plasticity in brain areas associated with motor execution. However, it is still challenging to obtain reliable information regarding improvement in neural functioning during rehabilitation and its neuro-physiological dynamics. Brain connectivity is a reliable biomarker associated with brain functionality. Here, we evaluate to what extent partial granger causality can provide information in form of effective neural connectivity that can differentiate motor imagery tasks. Our results on nine subjects using the EEG dataset (BCI competition 2008 dataset 2A) show distinct connectivity patterns for all four MI classes, and higher information flow in the fronto-parietal network during task phase as compared to non-task phase. The results support the conclusion that effective connectivity analysis through partial granger causality can provide key information about neural interactions specific to different MI tasks. Moreover these interactions can be utilized as reliable biomarkers for assessment of motor recovery during stroke rehabilitation.

A major issue for bringing brain-computer interface (BCI) based on electroencephalogram (EEG) recordings outside of laboratories is the non-stationarities of EEG signals. Varying statistical properties of the signals during inter- or intra-session transfers can lead to deteriorated BCI performances over time. These variations may cause the input data distribution to shift when transitioning from the training phase (calibration session) to the testing/operating phase resulting in a covariate shift. We propose to handle this issue using a novel hybrid learning method based on two classifiers, wherein the first classifier allows including new information in the training dataset, and the second classifier performs an overall classification. The proposed method is motivated by the smoothness assumption, i.e., the points that are closest to each other are more likely to share the same label, and may be added online to enrich the training dataset. The method is evaluated on two real-world datasets corresponding to motor imagery detection (BCI competition 2008 dataset 2A and 2B). The results support the conclusion that an improvement in the classification accuracy over traditional inductive learning and semi-supervised learning methods can be obtained.

People suffering from a variety of upper and lower limb disabilities due to different neuro-muscular diseases or injuries, often find it difficult to perform day-to-day activities of mobility and grasping (pick and place) objects. This paper presents the feasibility and utility of a newly developed assistive device named EMOHEX, for disabled people to perform some activities of daily living (ADL). EMOHEX is an integrated platform that combines a low cost eye-tracking device with a powered-wheelchair mounted hand-exoskeleton, which can assist disabled people in grasping objects while moving around. A dual control panel based graphical user interface is designed wherein the user's intention to select any command button is detected through eye-tracking. The dual control consists of wheelchair control panel and exoskeleton control panel, which are interchangeable by a switch button common to both the panels. The hand-exoskeleton is capable of assisting grasp, hold, and release action. Experiments conducted on 16 healthy participants revealed that performance metrics were significantly (p<;0.01) similar for the same task complexity while for different task complexities the performance metrics were significantly (p<;0.01) different across all the participants. These results showed the feasibility and stability of the system, respectively. Moreover, the information transfer rate (ITR) of eye-tracker was found satisfactory at 55.28±1.29 bits/min and 51.02±1.72 bits/min for simple and complex task, respectively. Thus, EMOHEX has the potential as a quality assistive device for disabled people.

Over the last decade, there has been a gradual increase in the number of people with mobility and speech impairments who require novel communication devices. Most of the recent works focus on the Latin script; there is a lack of appropriate assistive devices for scripts that are specific to a country. In this paper, we propose a novel multimodal Hindi language virtual keyboard based on a menu selection with eight commands providing access to spell and type 63 different Hindi language characters along with other functionalities such as the delete command for corrections. The system has been evaluated with eight able-bodied individuals who performed a specially designed typing task. The spelling task has been achieved in three different modalities using: (i) a mouse, (ii) a portable eye-tracker, and (iii) a portable eye-tracker combined with a soft-switch. The performance has been evaluated over the changes that occur with the use of each modality in terms of typing speed and information transfer rate (ITR) at both the command and letter levels for each participant. The average speed across participants with mouse only, eye-tracker only, and eye-tracker with soft-switch were 17.12 letters/min, 10.62 letters/min, and 13.50 letters/min, respectively. The ITRs at the command and letter levels were about 67.58 bits/minute and 62.67 bits/minute, respectively, with only the eye-tracker option. Based on its robustness, the proposed system has the potential to be used as a means of augmentative communication for patients suffering from mobility and speech impairment, and can contribute to substantial improvement in their quality of life.

A common assumption in traditional supervised learning is the similar probability distribution of data between the training phase and the testing/operating phase. When transitioning from the training to testing phase, a shift in the probability distribution of input data is known as a covariate shift. Covariate shifts commonly arise in a wide range of real-world systems such as electroencephalogram-based brain–computer interfaces (BCIs). In such systems, there is a necessity for continuous monitoring of the process behavior, and tracking the state of the covariate shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and -adaptation methodology, and its application to motor imagery-based BCIs. A covariate shift-detection test based on an exponential weighted moving average model is used to detect the covariate shift in the features extracted from motor imagery-based brain responses. Following the covariate shift-detection test, the methodology initiates an adaptation by updating the classifier during the testing/operating phase. The usefulness of the proposed method is evaluated using real-world BCI datasets (i.e. BCI competition IV dataset 2A and 2B). The results show a statistically significant improvement in the classification accuracy of the BCI system over traditional learning and semi-supervised learning methods.

The detection of brain responses corresponding to the presentation of a particular class of images is a challenge in Brain-Machine Interface (BMI). Brain decoding is nowadays possible thanks to advanced brain recording devices (fMRI, EEG, MEG), and the use of appropriate signal processing and machine learning techniques. Current systems based on the detection of brain responses during rapid serial visual presentation (RSVP) tasks use EEG recording. We propose to evaluate the performance of single-trial detection with signal recorded with magnetoencephalography (MEG) during an RSVP task where participants were asked to detect images containing a person. We compare several classifiers (LDA, BLDA, k-nearest neighbor, support-vector machines) with spatial filtering, and with different sets of channels (magneto-meters, gradio-meters, all the channels). The results suggest that single-trial detection can be obtained with an AUC superior to 0.95, while typical studies based on EEG recordings using the same type of tasks, the AUC is often around 0.8. The present results show that MEG can be successfully used for target detection during a difficult RSVP task.

In this paper, we present a new filtering method based on the empirical mode decomposition (EMD) for classification of motor imagery (MI) electroencephalogram (EEG) signals for enhancing brain-computer interface (BCI). The EMD method decomposes EEG signals into a set of intrinsic mode functions (IMFs). These IMFs can be considered narrow-band, amplitude and frequency modulated (AM-FM) signals. The mean frequency measure of these IMFs has been used to combine these IMFs in order to obtain the enhanced EEG signals which have major contributions due to μ and β rhythms. The main aim of the proposed method is to filter EEG signals before feature extraction and classification to enhance the features separability and ultimately the BCI task classification performance. The features namely, Hjorth and band power features computed from the enhanced EEG signals, have been used as a feature set for classification of left hand and right hand MIs using a linear discriminant analysis (LDA) based classification method. Significant superior performance is obtained when the method is tested on the BCI competition IV datasets, which demonstrates the effectiveness of the proposed method.

Learning in the presence of dataset shifts in non-stationary environments is a major challenge. Dataset shifts in the form of covariate shifts commonly occur in a broad range of real-world systems such as, electroencephalogram (EEG) based brain-computer interfaces (BCIs). Under covariate shifts, the properties of the input data distribution may shift over time from training to test/operating phase. In such systems, there is a need for continuous monitoring of the process behavior and tracking the state of the shifts to decide about initiating adaptation in a timely manner. This paper presents a covariate shift-detection and adaptation methodology, and its application to motor-imagery based BCIs. An exponential weighted moving average (EWMA) model based test is used for the covariate shift-detection in the features of EEG signals. The proposed algorithm initiates the adaptation by reconfiguring the knowledge-base of the classifier. Its performance is evaluated through experiments using a real-world dataset i.e. BCI Competition IV dataset 2A. Results show that the proposed methodology effectively performs covariate-shift-detection and adaptation and it can help to realize adaptive BCI systems.

A major problem in a brain-computer interface (BCI) based on electroencephalogram (EEG) recordings is the varying statistical properties of the signals during inter- or intra-session transfers that often lead to deteriorated BCI performances. A filter bank CSP (FBCSP) algorithm typically uses all the features from all the bands to extract and select robust features. In this paper, we evaluate the performance of four methods for frequency band selection applied to binary motor imagery classification: forward-addition (FA), backward-elimination (BE), the intersection and the union of the FA and BE. These methods automatically select and learn the best discriminative sets of frequency bands, and their corresponding CSP features. The performances of the proposed methods are evaluated on binary motor imagery classification using a publicly available real-world dataset (BCI competition 2008 dataset 2A). It is found that the BE method provides the best improvement resulting in an average classification accuracy increase of the BCI system over the FBCSP algorithm, from 77.06% to 79.09%.

Non-invasive Brain-Computer Interface (BCI) has appeared as a new hope for a large population of disabled people, who were waiting for a new communication means that would translate some brain responses into actions. After several decades of research in fields such as neuroscience and machine learning, the performance remains too low due to the low signal to noise ratio of the EEG signal, and the time that has to be dedicated to the recording of the brain responses. Hybrid BCIs consider the combination of several modalities, including brain responses, for new communication systems. The creation of a Hybrid BCI requires particular care as it possesses the constraints from several modalities. We propose to investigate the performance that could be achieved in a paradigm, where gaze control is used for the selection of an item on a computer screen and motor imagery is used to enable the selected item on the screen. Based on the results obtained from gaze detection with an eye tracker, and motor imagery detection with non-invasive EEG recording, we show that the performance of a parallel Hybrid BCI is only beneficial if the accuracy of each modality reaches a particular limit, and if the number of commands from each modality is carefully chosen.

A major challenge to devising robust brain-computer interfaces (BCIs) based on electroencephalogram (EEG) data is the immanent non-stationary characteristics of EEG signals. Statistical properties of the signals may shift during inter-or-intra session transfers that often leads to deteriorated BCI performance. The shift in the input data distribution from training to testing phase is called a covariate shift. It can be caused by various reasons such as different electrode placements, varying impedances and other ongoing brain activities. We propose an algorithm to handle this issue by adapting to the covariate shifts in the EEG data using a transductive learning approach. The performance of the proposed method is evaluated on the BCI competition 2008-Graz dataset B. The results show an improvement in classification accuracy of the BCI system over a traditional learning method. The obtained results support the conclusion that covariate-shift-adaptation using transductive learning is helpful to realize adaptive BCI systems.

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

A major challenge in two-class brain-computer interface (BCI) systems is the low bandwidth of the communication channel, especially while communicating and controlling assistive devices, such as a smart wheelchair or a telepresence mobile robot, which requires multiple motion command options in the form of forward, left, right, backward, and start/stop. To address this, an adaptive user-centric graphical user interface referred to as the intelligent adaptive user interface (iAUI) based on an adaptive shared control mechanism is proposed. The iAUI offers multiple degrees-of-freedom control of a robotic device by providing a continuously updated prioritized list of all the options for selection to the BCI user, thereby improving the information transfer rate. Results have been verified with multiple participants controlling a simulated as well as physical pioneer robot.

Dataset shift is a major challenge in the non-stationary environments wherein the input data distribution may change over time. Detecting the dataset shift point in the time-series data, where the distribution of time-series changes its properties, is of utmost interest. Dataset shift exists in a broad range of real-world systems. In such systems, there is a need for continuous monitoring of the process behavior and tracking the state of the shift so as to decide about initiating adaptive corrections in a timely manner. This paper presents an algorithm to detect the shift-point in a non-stationary time-series data. The proposed method detects the shift-point based on an exponentially weighted moving average (EWMA) control chart for auto-correlated observations. This algorithm is suitable to be run in real-time and monitors the data to detect the dataset shift. Its performance is evaluated through experiments using synthetic and real-world datasets. Results show that all the dataset-shifts are detected without the delay.

Brain-computer interface (BCI) technology is a means of communication that allows individuals with severe movement disability to communicate with external assistive devices using the electroencephalogram (EEG) or other brain signals. This paper presents an alternative neural information processing architecture using the Schrödinger wave equation (SWE) for enhancement of the raw EEG signal. The raw EEG signal obtained during the motor imagery (MI) of a BCI user is intrinsically embedded with non-Gaussian noise while the actual signal is still a mystery. The proposed work in the field of recurrent quantum neural network (RQNN) is designed to filter such non-Gaussian noise using an unsupervised learning scheme without making any assumption about the signal type. The proposed learning architecture has been modified to do away with the Hebbian learning associated with the existing RQNN architecture as this learning scheme was found to be unstable for complex signals such as EEG. Besides, this the soliton behaviour of the non-linear SWE was not properly preserved in the existing scheme. The unsupervised learning algorithm proposed in this paper is able to efficiently capture the statistical behaviour of the input signal while making the algorithm robust to parametric sensitivity. This denoised EEG signal is then fed as an input to the feature extractor to obtain the Hjorth features. These features are then used to train a Linear Discriminant Analysis (LDA) classifier. It is shown that the accuracy of the classifier output over the training and the evaluation datasets using the filtered EEG is much higher compared to that using the raw EEG signal. The improvement in classification accuracy computed over nine subjects is found to be statistically significant.

The raphe nucleus in the brain is the main source of serotonin (5-HT), an important brain chemical in regulating mood, cognition and behavior. This paper presents a spiking neuronal network model of the dorsal region of the raphe nucleus (DRN). We solve the perplexing problem of heterogeneous spiking neuronal behavior observed in the DRN by using an adaptive quadratic integrate-and-fire neuronal model and varying only its membrane potential reset after a spike, suggesting a potential role of certain recovery ionic currents. Specifically, the model can mimic the effects of slow afterhyperpolarization current and control the production of spikes per burst as found in experiments. Our model predicts specific input-output functions of the neurons which can be experimentally tested. Phase-plane analysis confirms their spiking dynamics. By coupling the 5-HT neurons with non-5-HT inhibitory neurons, we show that the DRN neuronal spiking activities recorded in behaving monkeys can generally be reproduced by adopting a feedforward inhibitory network architecture. Our model further predicts a low frequency network oscillation (about 8 Hz) among non-5-HT neurons around the rewarding epoch of a simulated experimental trial, which can be verified through direct recordings in behaving animals. Our computational model of the DRN accounts for the heterogeneous spiking patterns found in experiments, suggests plausible network architecture, and provides model predictions which can be directly tested in experiments. The model conveniently forms the basis for building extended network models to study complex interactions of the 5-HT system with other brain regions.

The non-stationary nature of the electroencephalogram (EEG) poses a major challenge for the successful operation of a brain-computer interface (BCI) when deployed over multiple sessions. The changes between the early training measurements and the proceeding multiple sessions can originate as a result of alterations in the subject's brain process, new cortical activities, change of recording conditions and/or change of operation strategies by the subject. These differences and alterations over multiple sessions cause deterioration in BCI system performance if periodic or continuous adaptation to the signal processing is not carried out. In this work, the covariate shift is analyzed over multiple sessions to determine the non-stationarity effects and an unsupervised adaptation approach is employed to account for the degrading effects this might have on performance. To improve the system's online performance, we propose a covariate shift minimization (CSM) method, which takes into account the distribution shift in the feature set domain to reduce the feature set overlap and unbalance for different classes. The analysis and the results demonstrate the importance of CSM, as this method not only improves the accuracy of the system, but also reduces the classification unbalance for different classes by a significant amount.

In this work the self-organizing fuzzy neural network (SOFNN) is employed to create an accurate and easily calibrated approach to multiple-step-ahead prediction for the NN5 forecasting competition 2008. The competition dataset consists of 111 daily empirical time series of cash-machine withdrawals. The objective for the competition was to forecast future transactions up to 56 days ahead with the highest prediction accuracy using a single methodology. The SOFNN is a highly efficient and accurate algorithm for time series-prediction which learns from data incrementally and can autonomously adapt its structure in the learning process to cope with drifts in the data dynamics. It can also modify its architecture autonomously to suit different prediction horizons, embedding dimensions and time lags. Standard neural networks(NNs) and autoregressive(AR) models are employed as benchmarks for comparison. It is shown through a statistical analysis of the results, that the SOFNN significantly outperforms the NN and AR methods.

This paper presents an approach that permits the effective hardware realization of a novel Evolvable Spiking Neural Network (ESNN) paradigm on Field Programmable Gate Arrays (FPGAs). The ESNN possesses a hybrid learning algorithm that consists of a Spike Timing Dependent Plasticity (STDP) mechanism fused with a Genetic Algorithm (GA). The design and implementation direction utilizes the latest advancements in FPGA technology to provide a partitioned hardware/software co-design solution. The approach achieves the maximum FPGA flexibility obtainable for the ESNN paradigm. The algorithm was applied as an embedded intelligent system robotic controller to solve an autonomous navigation and obstacle avoidance problem.

A novel adaptive critic based kinematic control scheme for a redundant manipulator has been proposed in this paper. The redundancy resolution has been formulated as a discrete-time optimal control problem. A Takagi-Sugeno (T-S) fuzzy based critic network is proposed to predict the global costate dynamics as fuzzy average of local costate dynamics. The integral cost function is used in the literature earlier, to achieve global optimum in contrast to instantaneous cost functions which gives local optimum. But both the approaches require the computation of pseudo-inverse which is computationally complex and suffers from numerical instability. In contrast, the proposed scheme does not require computation of pseudo-inverse of the Jacobian which makes the method computationally efficient. The proposed scheme is tested on 7 degree of freedom (7DOF) PowerCube manipulator from Amtec Robotics.

This paper proposes a novel scheme of a Takagi-Sugeno (T-S) fuzzy based adaptive critic for the optimal control of the continuous-time input affine nonlinear system. A novel learning strategy is proposed to update the weights of critic network which resolves the issue of under-determined weight update equations discussed in [1]. The T-S Fuzzy based critic network approximates the global optimal cost as fuzzy average of local costs associated with local linear subsystems. This work clearly demonstrates that the optimal cost of a nonlinear system can be represented as the fuzzy cluster of optimal costs of locally valid linear models in a T-S framework. The proposed scheme has been simulated for four different dynamic systems. Simulation results clearly demonstrate that the T-S Fuzzy approximates the optimal cost, with subsystems in each fuzzy zone represents the optimal cost of locally valid linear model.

Distinct features play a vital role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, numerous parameters, such as separable frequency bands, data acquisition channels and time point of maximum separability are chosen explicit to each subject. Recent research has shown that using subject specific parameters for the extraction of invariant characteristics specific to each brain state can significantly improve the performance and accuracy of a brain-computer interface (BCI). This paper focuses on developing a fast autonomous user-specific tuned BCI system using particle swarm optimization (PSO) to search for optimal parameter combination based on the analysis of the correlation between different classes i.e., the R-squared (R2) correlation coefficient rather than assessing overall systems performance via performance measure such as classification accuracy. Experimental results utilizing eight subjects are presented which demonstrate the effectiveness of the proposed methods for fast & efficient user-specific tuned BCI system.

This paper introduces a number of modifications to the learning algorithm of the self-organizing fuzzy neural network (SOFNN) to improve computational efficiency. It is shown that the modified SOFNN favorably compares to other evolving fuzzy systems in terms of accuracy and structural complexity. An analysis of the SOFNN's effectiveness when applied in an electroencephalogram (EEG)-based brain-computer interface (BCI) involving the neural-time-series-prediction-preprocessing (NTSPP) framework is also presented, where a sensitivity analysis (SA) of the SOFNN hyperparameters was performed using EEG data recorded from three subjects during left/right-motor-imagery-based BCI experiments. The aim of this one-time SA was to eliminate the need to choose subject- and signal-specific hyperparameters for the SOFNN and thus apply the SOFNN in the NTSPP framework as a parameterless self-organizing framework for EEG preprocessing. The results indicate that a general set of NTSPP parameters chosen via the SA provide the best results when tested in a BCI system. Therefore, with this general set of SOFNN parameters and its self-organizing structure, in conjunction with parameterless feature extraction and linear discriminant classification, a fully parameterless BCI that lends itself well to autonomous adaptation is realizable.

Recent research has shown that neural networks (NNs) or self-organizing fuzzy NNs (SOFNNs) can enhance the separability of motor imagery altered electroencephalogram (EEG) for brain-computer interface (BCI) systems. This is achieved via the neural-time-series-prediction-preprocessing (NTSPP) framework where SOFNN prediction models are trained to specialize in predicting the EEG time-series recorded from different EEG channels whilst subjects perform various mental tasks. Features are extracted from the predicted signals produced by the SOFNN and it has been shown that these features are easier to classify than those extracted from the original EEG. Previous work was based on a two class BCI. This paper presents an analysis of the NTSPP framework when extended to operate in a multiclass BCI system. In mutliclass systems normally multiple EEG channels are used and a significant amount of subject-specific parameters and EEG channels are investigated. This paper demonstrates how the SOFNN-based NTSPP, tested in conjunction with three different feature extraction procedures and different linear discriminant and support vector machine (SVM) classifiers, is effective in improving the performance of a multiclass BCI system, even with a low number of standardly positioned electrodes and no subject-specific parameter tuning.

Type-2 (T2) fuzzy logic (FL) systems (T2FLSs) have shown a remarkable potential in dealing with uncertain data resulting from real-world systems with non-stationary characteristics. This paper reports on novel developments in interval T2FLS (IT2FLS) classifier design methodology so that system non-stationarities can be effectively handled. In general, the approach presented here rests on a general concept of two-stage FLS design in which an initial rule base structure is first initialized and then system parameters are globally optimized. The proposed incremental enhancements of existing fuzzy techniques, adopted from the area of conventional type-1 (T1) FL, are heuristic in nature. The IT2FLS design methods have been empirically verified in this work in the realm of pattern recognition. In particular, the potential and the suitability of IT2FLS to the problem of classification of motor imagery (MI) related patterns in electroencephalogram (EEG) recordings has been investigated. The outcome of this study bears direct relevance to the development of EEG-based brain-computer interfaces (BCIs) since the problem under examination poses a major difficulty for the state-of-the-art BCI methods. The IT2FLS classifier is evaluated in this work on multi-session EEG data sets in the framework of an off-line BCI. Its performance is quantified in terms of the classification accuracy (CA) rates and has been found to be favorable to that of analogous systems employing a conventional T1FLS, along with linear discriminant analysis (LDA) and support vector machine (SVM), commonly utilized in MI-based BCI systems.

We present a method for recognizing individuals from their ldquostyle of actionrdquo. Two forms of human recognition can be useful: the determination that an object is from the class of humans (which is called human detection), and the determination that an object is a particular individual from this class (this is called individual recognition). This paper focuses on the latter problem. A periodicity is detected in from a sequence of motion detected binary image frames by finding the maximum similarity measure between them. Based on the periodicity information the Motion History Image (MHI) is applied for each individual sequence to find out entire motion information of periodic action. The individual is then recognized using a partial Hausdorff Distance similarity measure and the SVM classification approach.

The significance of the initialization procedure in the development of Type-2 fuzzy logic (T2FL) system-based classifiers should be highlighted considering their intrinsically non-linear nature. Initial structure identification has been recognized as a crucial stage in the design of an interval T2FL (IT2FL) classifier utilized in the framework of electroencephalogram (EEG)-based brain -computer interface (BCI). In conjunction with an efficient gradient-based learning algorithm it has allowed for robust exploitation of T2FL's capabilities to effectively handle uncertainties inherently associated with changing dynamics of electrical brain activity. This paper builds on the previous experiences in tackling the problem of inter-session classification of motor imagery (MI)-related EEG patterns. The major contribution of this work is an empirical investigation of the concept of support vector (SV) learning applied to structure identification of the IT2FL classifier. The SV-enhanced initialization scheme is found to compare favorably to both an arbitrary initialization and the clustering approach utilized in the preceding work in terms of the inter-session BCI classification performance of the fully trained IT2FLS evaluated on three subjects.

Autonomic computing is gradually becoming accepted as a viable approach to achieving self- management in systems and networks, with the goal of lessening the impact of the complexity crisis on the computing industry. The authors propose the integration of high level self-organisation features into the Application Directed Adaptive Framework (ADAF) which, when embedded into software applications, enables those applications to exhibit autonomic behaviour. This paper briefly discusses the infrastructure of ADAF and demonstrates two self-managing capabilities, namely self- monitoring and self-diagnosis.

When symbolic AI approaches are applied to handle continuous valued attributes, there is a requirement to transform the continuous attribute values to symbolic data. In this paper, a novel distribution-index-based discretizer is proposed for such a transformation. Based on definitions of dichotomic entropy and a compound distributional index, a simple criterion is applied to discretize continuous attributes adaptively. The dichotomic entropy indicates the homogeneity degree of the decision value distribution, and is applied to determine the best splitting point. The compound distributional index combines both the homogeneity degrees of attribute value distributions and the decision value distribution, and is applied to determine which interval should be split further; thus, a potentially improved solution of the discretization problem can be found efficiently. Based on multiple reducts in rough set theory, a multiknowledge approach can attain high decision accuracy for information systems with a large number of attributes and missing values. In this paper, our discretizer is combined with the multiknowledge approach to further improve decision accuracy for information systems with continuous attributes. Experimental results on benchmark data sets show that the new discretizer can improve not only the multiknowledge approach, but also the naive Bayes classifier and the C5.0 tree

This paper presents a number of enhancements to the self-organizing fuzzy neural network (SOFNN). Firstly, the SOFNN is described and a modification to the learning algorithm to improve computational efficiency is introduced. Secondly, a sensitivity analysis (SA) of the predefined SOFNN parameters is presented using electroencephalogram (EEG) data recorded from three subjects during left/right motor imagery-based brain-computer interface (BCI) experiments. This SA was carried out to determine if a general set of parameters could be used for predicting various non-stationary EEG time-series dynamics for multiple subjects. The SOFNN modifications significantly enhance computational efficiency and the SA results suggest that it may be possible to select a general set of parameters for different motor imagery-based EEG signals thus potentially enhancing the SOFNNs autonomy for application in a BCI.

A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. A new adding method based on geometric growing criterion and the epsiv-completeness of fuzzy rules is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters including the number of fuzzy rules. This has two steps: First, the linear parameter matrix is adjusted, and second, the centers and widths of all membership functions are modified. The GA is introduced to identify the least important neurons, i.e., the least important fuzzy rules. Simulations are presented to illustrate the performance of the proposed algorithm

A self-organizing computing network based on concepts of fuzzy conditions, beliefs, probabilities, and neural networks is proposed for decision-making in intelligent systems which are required to handle data sets with a diversity of data types. A sense-function with a sense-range and fuzzy edges is defined as a transfer function for connections from the input layer to the hidden layer in the network. By generating hidden cells and adjusting the parameters of the sense-functions, the network self-organizes and adapts to a training set. Computing cells in the input layer are designed as data converters so that the network can deal with both symbolic data and numeric data. Hidden computing cells in the network can be explained via fuzzy rules in a similar manner to those in fuzzy neural networks. The values in the output layer can be explained as a belief distribution over a decision space. The final decision is made by means of the winner-take-all rule. The approach was applied to a series of the benchmark data sets with a diversity of data types and comparative results obtained. Based on these results, the suitability of a range of data types for processing by different intelligent techniques was analyzed, and the results show that the proposed approach is better than other approaches for decision-making in information systems with mixed data types.

In this paper, an evolving learning mechanism is proposed for general computing network model to make decisions in intelligent systems. The novel mechanism is performed by means of a set of computing cell operations such as self-generation, growth, self-division, and death. Under the mechanism, a computing network grows up to a mature network. A hidden cell in the network is defined as a condition matching-unit in response to a fuzzy sub-superspace in multiple-dimension input superspace. A sense-function is defined to represent connections from a hidden cell to input cells. The range and edge vagueness of the sense-function are determined by evolving learning mechanism when sample instances are presented to the network. This network is able to learn from a very few training instances to make decisions for unseen instances. The benchmark data sets from the UCI machine learning repository are applied to test the network and comparable results are obtained.

Embedded systems are an integral part of industrial and domestic electronics necessitating educational institutions and other training providers to offer advanced embedded systems courses. Effective teaching in this area requires an interdependent approach combining theoretical material underpinned by practical laboratory experiments and individualized tutoring. Increasingly the courses on offer include web based distance education packages augmented by the provision of remote experimentation laboratories facilitating distant access to campus based physical resources. The degree of functionality and level of user access to remote laboratories has evolved in recent years expedited by advances in web applications and technologies. This paper details our recent work in this area focusing on the development of architecture for an integrated remote experimentation laboratory designed to meet the unique requirements of a complex, dynamic learning environment.

A novel hybrid algorithm based on the genetic algorithm, named self-organizing fuzzy neural network based on genetic algorithm (SOFNNGA), is proposed to design a fuzzy neural network to implement Takagi-Sugeno (TS) type fuzzy models in this paper. A new adding method based on geometric growing criterion and the /spl epsiv/-completeness of fuzzy rules is used to generate the initial structure firstly. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters, which has two steps: first, adjusting the parameter matrix, and second, centers and widths of all membership functions are modified. A simulation for a benchmark problem is presented to illustrate the performance of the proposed algorithm.

Case Based Reasoning System (CBR) is an efficient and straightforward approach for recommending desirable product formulations. In response to a changeable market situation, it is vital to adapt or generate enhanced and more efficient product formulations and manufacturing routes. Due to the lack of variability and indefinite data distribution in many case libraries, however, this technique can be very limited and is inadequate for creating improved formulations. The proposed approach represents an intelligent way to overcome this shortcoming. Following a carefully premeditated Design of Experiment (DOE), a synergistic use of data noise removal, modeling and optimization techniques were utilized to develop favorable product formulations. After a multivariate validation, these along with DOE based formulations are retained in the CBR library for later use.

There are several key attributes within the medical packaging industry, such as, anchorage, seal strength etc, which characterize the quality of the medical packaging product. This paper presents a new approach to determine the anchorage of the packaging material. The conventional approach to anchorage measurement involves a manual visual inspection procedure, which is very subjective and has led to inconsistencies in quality control. A new proposed method to improve this procedure involves image-processing technique, which eliminates operator dependencies. A fuzzy reasoning approach is then used to quantify the results generated from image processing which replaces the human decision process. This fusion of these techniques results in a more automated systematic approach to anchorage measurement. Experimental studies confirm that this novel approach leads to a more robust and improved quality control system for the medical packaging company.