Brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) are widely applied in target detection but suffer from low decoding accuracy. While some methods integrate eye movements to improve localization, they often underutilize EEG’s coarse spatial cues, limiting overall detection effectiveness. We propose a brain-eye collaborative method for camouflaged target detection that integrates EEG and eye movement signals in a coarse-to-fine framework. In the coarse stage, target presence and its image quadrant are determined using brain-eye fusion and contrastive learning on bimodal features. This leverages complementary spatiotemporal information from both modalities to generate a coarse localization region. In the fine stage, eye movement data further refines the target location within this region by identifying high-interest areas. This collaborative approach significantly improves both recognition and localization performance in complex visual search tasks. Our method achieves an F1 score of 83.16% and a balanced accuracy of 85.38%, outperforming existing state-of-the-art methods.

Machine learning, particularly deep learning, typically achieves high facial emotion image recognition accuracy benefiting from multiple labeled data. However, the datasets usually contain insufficient labeled samples and numerous unlabeled data since human labeling is a costly endeavor. For semi-supervised learning of these datasets, self-training procedure solely based on the visual features of images fails to comprehensively understand the intricate high-level semantic features. Since EEG signals contain not only visual information related to the visual stimulus but also emotional information related to brain activity, they are highly suitable as supervisory signals for labeling unlabeled facial emotion images. In this study, we specifically employ EEG signals evoked by visual image stimuli in conjunction with EEGNet3D to learn a discriminative EEG class representation manifold of brain activity. The one-hot class label is replaced with the EEG class representation as the supervisory to train the base model. Then, better pseudo-labeling is achieved using the base model in the EEG class representation manifold. Based on pseudo-labeling results, the utilization of unlabeled data is further improved. Interestingly, our findings reveal that when utilizing EEG class representations as supervisory information for the base model, the base model demonstrates a learning pattern that involves focusing more on the eye area when making judgments about emotions. This behavior closely resembles how the human brain decodes emotions. Experiments show that the performance of the proposed method can be effectively enhanced by combining labeled and pseudo-labeled images. Further experiments demonstrate that our method exhibits strong generalization abilities when applied to new image datasets and other visual networks.

The multi-user motor imagery brain-computer interface (BCI) is a new approach that uses information from multiple users to improve decision-making and social interaction. Although researchers have shown interest in this field, the current decoding methods are limited to basic approaches like linear averaging or feature integration. They ignored accurately assessing the coupling relationship features, which results in incomplete extraction of multi-source information. To overcome these limitations, we propose a new reinforcement learning electroencephalography (EEG) decoding method based on mutual information mechanisms. Our method enhances the extraction of multi-source common information and uses a dynamic feedback model for inter-brain mutual information reward and punishment mechanisms in the reinforcement learning channel selection module. We feed the single-brain and inter-brain signals after channel selection into deep neural networks, which automatically extract coupled features. Finally, based on the attention indices calculated from EEG signals at prefrontal electrode positions, the output is obtained by voting. Our experimental results show that the average accuracy of dual-brain recognition is improved by 16% compared to single-brain mode. Furthermore, ablation experiments demonstrate that the reinforcement learning module and attention voting module enhance accuracy by 14.5% and 15.7%, respectively.

Neural network models of machine learning have shown promising prospects for visual tasks, such as facial emotion recognition (FER). However, the generalization of the model trained from a dataset with a few samples is limited. Unlike the machine, the human brain can effectively realize the required information from a few samples to complete the visual tasks. To learn the generalization ability of the brain, in this article, we propose a novel brain-machine coupled learning method for facial emotion recognition to let the neural network learn the visual knowledge of the machine and cognitive knowledge of the brain simultaneously. The proposed method utilizes visual images and electroencephalogram (EEG) signals to couple training the models in the visual and cognitive domains. Each domain model consists of two types of interactive channels, common and private. Since the EEG signals can reflect brain activity, the cognitive process of the brain is decoded by a model following reverse engineering. Decoding the EEG signals induced by the facial emotion images, the common channel in the visual domain can approach the cognitive process in the cognitive domain. Moreover, the knowledge specific to each domain is found in each private channel using an adversarial strategy. After learning, without the participation of the EEG signals, only the concatenation of both channels in the visual domain is used to classify facial emotion images based on the visual knowledge of the machine and the cognitive knowledge learned from the brain. Experiments demonstrate that the proposed method can produce excellent performance on several public datasets. Further experiments show that the proposed method trained from the EEG signals has good generalization ability on new datasets and can be applied to other network models, illustrating the potential for practical applications.

Epilepsy is a chronic disease caused by sudden abnormal discharges of neurons which lead to transient brain disfunction. For those patients who need surgery, the recognition of the epileptic foci is one of the most important steps. In this paper, a combination of EEG signals processed by Short-time Fourier Transform and processed by Continuous Wavelet Transform is introduced. A deep learning model with an accuracy of 91.3% trained by Convolution Neural Network has been obtained. The experiment uses a methodology that has not been widely tested so far, and it also demonstrated the practical values by its relatively small time consumption and computing power requirements meanwhile ensuring high accuracy. Furthermore, it proves the feasibility of improving accuracy by combining different feature extraction methods.

Epileptic focus localization in patients with epileptic seizures is essential when surgery is needed. Recent studies show that this can be done automatically using machine learning approaches. However, well-designed feature extraction methods are often computationally demanding, requiring a large amount of data labeled by physicians, which is time consuming and impractical. In this paper, we firstly introduce a one-dimensional convolutional neural network (1D-CNN) model for epileptic seizure focus detection which avoids the manual, time-consuming feature extraction Moreover, to reduce the necessary number of training samples, we introduce an approach for data augmentation. The experimental results demonstrate the efficiency of the proposed method, with a nearly 3% improvement in performance using the data enhancement method compared to the best result obtained using the traditional feature extraction method.

In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity. In particular, for tensor ring (TR) decomposition, the number of model possibilities grows exponentially with the tensor order, which makes it rather challenging to find the optimal TR decomposition. In this paper, by exploiting the low-rank structure of the TR latent space, we propose a novel tensor completion method which is robust to model selection. In contrast to imposing the low-rank constraint on the data space, we introduce nuclear norm regularization on the latent TR factors, resulting in the optimization step using singular value decomposition (SVD) being performed at a much smaller scale. By leveraging the alternating direction method of multipliers (ADMM) scheme, the latent TR factors with optimal rank and the recovered tensor can be obtained simultaneously. Our proposed algorithm is shown to effectively alleviate the burden of TR-rank selection, thereby greatly reducing the computational cost. The extensive experimental results on both synthetic and real-world data demonstrate the superior performance and efficiency of the proposed approach against the state-of-the-art algorithms.

Dimensionality reduction is an essential technique for multiway large-scale data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to its high representation ability and flexibility. However, the traditional TR decomposition algorithms suffer from high computational cost when facing large-scale data. In this paper, taking advantages of the recently proposed tensor random projection method, we propose two TR decomposition algorithms. By employing random projection on every mode of the large-scale tensor, the TR decomposition can be processed at a much smaller scale. The simulation experiment shows that the proposed algorithms are 4–25 times faster than traditional algorithms without loss of accuracy, and our algorithms show superior performance in deep learning dataset compression and hyperspectral image reconstruction experiments compared to the other randomized algorithms.

In this paper, we aim at the problem of tensor data completion. Tensor-train decomposition is adopted because of its powerful representation ability and linear scalability to tensor order. We propose an algorithm named Sparse Tensor-train Optimization (STTO) which considers incomplete data as sparse tensor and uses first-order optimization method to find the factors of tensor-train decomposition. Our algorithm is shown to perform well in simulation experiments at both low-order cases and high-order cases. We also employ a ten-sorization method to transform data to a higher-order form to enhance the performance of our algorithm. The results of image recovery experiments in various cases manifest that our method outperforms other completion algorithms. Especially when the missing rate is very high, e.g., 90% to 99%, our method is significantly better than the state-of-the-art methods.

EEG (electroencephalography) can be analyzed quantitatively by introducing the EEG energy index in EEG preliminary examination of determination of brain death. EMD (empirical mode decomposition), MEMD (multivariate empirical mode decomposition), and 2T-EMD (turning tangent empirical mode decomposition) can be used to analyze coma and quasi-brain-death patients' EEG energy. In this paper, EMD, MEMD, and 2T-EMD are compared from algorithm principle and experimental aspects in order to obtain the optimal algorithm for supporting determination of brain death, where experiments are carried out based on standard artificial signals and patients' EEG. The analysis results illustrate that 2T-EMD is the optimal algorithm with the relative superior computational performance for both single and multi-channel signals.

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful and useful data analysis technique. This method can achieve the purpose of tensor completion through explicitly capturing the multilinear latent factors. Recently, a CP factorization based on a hierarchical probabilistic model has been proposed which is used fully Bayesian theory by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyper-priors over all hyper-parameters. In this way, the rank of tensor can be determined automatically instead of traditional manual assignment. This method has been applied into image inpainting and facial image synthesis effectively. However, there is no research on the application in EEG signal processing of this method. Moreover, the EEG data loss often occurs during experiment recording period. In this paper, we used this newer data analysis method for processing EEG data set from P300 experiment including data completion under different levels of data missing and classification analysis on the recovered data. The experiment result shows that this method has a good processing performance on incomplete EEG signal.

Reducing noise in a video sequence is of vital important in many real-world applications. One popular method is block matching collaborative filtering. However, the main drawback of this method is that noise standard deviation for the whole video sequence is known in advance. In this paper, we present a tensor based denoising framework that considers 3D patches instead of 2D patches. By collecting the similar 3D patches non-locally, we employ the low-rank tensor decomposition for collaborative filtering. Since we specify the non-informative prior over the noise precision parameter, the noise variance can be inferred automatically from observed video data. Therefore, our method is more practical, which does not require knowing the noise variance. The experimental on video denoising demonstrates the effectiveness of our proposed method.

Brain computer interface (BCI) systems based on the steady state visual evoked potential (SSVEP) provide higher information transfer rates and require shorter training time than BCI systems using other brain signals. It has been widely used in brain science, rehabilitation engineering, biomedical engineering and intelligent information processing. In this paper, we present a real-time mobile phone dialing system based on SSVEP, and it is more portable than other dialing system because the flashing dial interface is set on a small tablet. With this online BCI system, we can take advantage of this system based on SSVEP to identify the specific frequency on behalf of a number using canonical correlation analysis (CCA) method and dialed out successfully without using any physical movements such as finger tapping. This phone dialing system will be promising to help disable patients to improve the quality of lives.

Analysis of electroencephalography (EEG) energy is a useful technique in the brain signal processing. In this paper, we present a novel data analysis method based on a dynamic multivariate empirical mode decomposition (D-MEMD) algorithm to analyze EEG energy of three different conscious states such as normal awake, comatose and brain death. By using D-MEMD, we can not only denoise the original EEG data but also calculate the EEG energy of subjects in a dynamic time series. Moreover, from the result, we distinguish three consciousness levels. The results of healthy subject in normal awake, comatose patient and brain death will be shown. The analyzed results illustrate the effectiveness and performance of the proposed method in calculation of EEG energy for evaluating consciousness level.

In this paper, we propose a Electroencephalography(EEG) signal processing method for the purpose of supporting the patient's EEG consciousness analysis. Approximate entropy(ApEn), as a complexity based method appears to have potential application to physiological and clinical time-series data. Therefore, we present an ApEn based statistical measure for patient's EEG consciousness analysis. However, it is found that high frequency noise such as electronic interference and its harmonic from the surrounding containing in the real-life recorded EEG lead to inconsistent ApEn result. To solve this problem, first we design a bandstop filter to filter high frequency noise. Then the proposed method is supported by analysis on a real world example of distinguishing between the brain consciousness states of coma and brain death. The experimental results demonstrate the effectiveness and performance of the proposed method in patient's EEG consciousness analysis.

Increasingly severe I/O bottlenecks on High-End Computing machines are prompting scientists to process simulation output data online while simulations are running and before storing data on disk. There are several options to place data analytics along the I/O path: on compute nodes, on separate nodes dedicated to analytics, or after data is stored on persistent storage. Since different placements have different impact on performance and cost, there is a consequent need for flexibility in the location of data analytics. The FlexIO middleware described in this paper makes it easy for scientists to obtain such flexibility, by offering simple abstractions and diverse data movement methods to couple simulation with analytics. Various placement policies can be built on top of FlexIO to exploit the trade-offs in performing analytics at different levels of the I/O hierarchy. Experimental results demonstrate that FlexIO can support a variety of simulation and analytics workloads at large scale through flexible placement options, efficient data movement, and dynamic deployment of data manipulation functionalities.

The multiplicative algorithms are well-known for nonnegative matrix and tensor factorizations. The ALS algorithm for canonical decomposition (CP) has been proved as a "work horse" algorithm for general multiway data. Unfortunately, for CP with nonnegativity constraints, this algorithm with a rectifier (projection) may not converge to the desired solution without additional regularization parameters in matrix inverses. The hierarchical ALS algorithm improves the performance of the ALS algorithm, outperforms the multiplicative algorithm. However, NTF algorithms can face problem with collinear or bias data. In this paper, we propose a novel algorithm which overwhelmingly outperforms all the multiplicative, and (H)ALS algorithms. By solving the nonnegative quadratic programming problems, a general algorithm of the HALS has been derived and experimentally confirmed its validity and high performance for normal and difficult bench marks, and for real-world EEG dataset.

A novel method to evaluate the statistical significance differences between the groups of coma and brain death patients is presented. This is achieved based on the electroencephalogram (EEG) and by using a collaborative filtering structure with the least mean square (LMS) and least mean phase (LMP) adaptive filters. By virtue of a complex-valued representation of pair-wise EEG signals, the evolution of the mixing parameter is used as an indicator of the fundamental amplitude-phase relationships of EEG recordings. Simulations illustrate the suitability of this approach to differentiate between the coma and quasi-brain-death states.

Electroencephalography (EEG) is widely used in evaluating the absence of cerebral cortex function for the determination of brain death. Since EEG recorded signal is always corrupted by some artefacts and various interfering noise, extracting active or nonactive features from noisy EEG signals and evaluating their significance is therefore crucial in the process of brain death diagnosis. This article presents an EEG-based preliminary examination system associated with empirical mode decomposition (EMD) technique to extract informative brain activity features from real-world recorded clinical EEG data. Moreover, the power spectrum technique is applied to evaluate the significant differences between the group of comatose patients and the group of quasi-brain-deaths. Our experimental results show effectiveness and some promising directions of applying the EMD method to the clinical EEG analysis.

It is important to estimate the driver dozing practically. In the conventional study, the electroencephalogram (EEG) has been a promising indicator to driver dozing. Furthermore, it is known that frequency of the EEG is highly related to the sleep and the wake conditions. Therefore, we extract frequency components of the EEG from the whole cerebral cortex, by using the rhythmic component extraction (RCE), proposed by Tanaka et al. RCE extracts a component by combining multi-channel signals with weights that are optimally sought for such that the extracted component maximally contains the power in the frequency range of interest and suppresses that in unnecessary frequencies. As a result, we confirmed the interested frequency power is emphasized. These results imply that this method is available for analyzing the sleeping.

A problem of information separation in multichannel recordings is important in engineering applications such as brain computer/machine interfaces (BCI/BMI). Whereas this problem is not entirely new, engineering approaches connecting the mental states of humans and the observed electroencephalography (EEG) recordings are still in their infancy, mostly due to problems with electrophysiological denoising. The electrophysiological signals captured in form of the EEG carry brain activity in form of the neurophysiological components which are usually embedded in much higher power electrical muscle activity components (electromyography - EMG; electrooculography - EOG; etc.). In this paper we present an approach to remove muscular interference caused by eye-movements from EEG recorded during auditory experiments in an eight channel recording setting. This is achieved by analyzing the correlation of the oscillatory modes within a multichannel signal in the Hilbert domain. Simulations in a real world auditory BCI setting support the analysis.

In this experiment, we record the EEG data during the sleeping and the awakening for the drowsiness cognition. As is well known, analyzing the frequency components of the EEG is important for the sleeping. There are many studies to analyze the EEG frequency data for recognizing the sleeping quality, and so on. However, there is no established method for the analysis of the sleeping EEG. From these reason, we are going to apply the rhythmic component extraction (RCE), proposed by Tanaka et al., to extract the rhythmic component in the brain. RCE finds a component extracted by using a weighted sum of observed channel signals. The weights are optimized by maximizing the power in a certain frequency range of interest. In the experiment, the subjects lie back, relax in a chair, and sleep. We take the EEG recording at 30 channels. From the results, the EEG features, such as the alpha and the theta wave, are different between the sleeping and the waking. These rhythmic components are extracted by the Fourier transform and the RCE. By using the RCE, we confirmed the alpha wave and the theta wave when they are not extracted in a single channel signal. Furthermore, we confirmed the effective measurement locations by analyzing the RCE weights.

In this paper, we propose a method for detecting the music to match a user’s mood in prefrontal cortex electroencephalogram (EEG) activity. The EEG frequencies analyzed are the components that contain significant and immaterial information. We focused on the combinations of the significant frequency. These frequency combinations are thought to express individual characteristics of EEG activity. In the proposed method, we calculate the percentage of the spectrum of these frequency combinations that does not include the noise frequency components and evaluates whether the music matches the user’s mood through a simple threshold processing. Then, a genetic algorithm (GA) is used to specify the frequency of individual characteristics on the EEG. Threshold values that used the threshold processing is determined in the GA. Finally, the performance of the proposed method is evaluated using real EEG data.

In this study, a novel way of processing observations before independent component analysis (ICA) using a Q-mode factor analysis (FA) was proposed for noisy blind source separation (BSS). The Q-mode analysis is a very efficient technique in classifying a data in cases where there are a large number of objects and where there is a little prior knowledge of the constituents. In the R-mode analyses, interrelationships between variables are analyzed. On the other hand, in the Q-mode analysis, interrelationships between objects are analyzed. Applying this approach to the experimental noisy data, we show that our proposed approach is more effective than the R-mode analysis for source separation of noisy data.

In this paper, sparse representation (factorization) of a data matrix is first discussed. An overcomplete basis matrix is estimated by using the K(cid:0)means method. We have proved that for the estimated overcom- plete basis matrix, the sparse solution (coefficient matrix) with minimum l1(cid:0)norm is unique with probability of one, which can be obtained using a linear programming algorithm. The comparisons of the l1(cid:0)norm so- lution and the l0(cid:0)norm solution are also presented, which can be used in recoverability analysis of blind source separation (BSS). Next, we ap- ply the sparse matrix factorization approach to BSS in the overcomplete case. Generally, if the sources are not sufficiently sparse, we perform blind separation in the time-frequency domain after preprocessing the observed data using the wavelet packets transformation. Third, an EEG experimental data analysis example is presented to illustrate the useful- ness of the proposed approach and demonstrate its performance. Two almost independent components obtained by the sparse representation method are selected for phase synchronization analysis, and their peri- ods of significant phase synchronization are found which are related to tasks. Finally, concluding remarks review the approach and state areas that require further study.

We propose a robust approach for independent component analysis (ICA) of signals where observations are contaminated with high-level additive noise and/or outliers. The source signals may contain mixtures of both sub-Gaussian and super-Gaussian components, and the number of sources is unknown. Our robust approach includes two procedures. In the first procedure, a robust prewhitening technique is used to reduce the power of additive noise, the dimensionality and the correlation among sources. A cross-validation technique is introduced to estimate the number of sources in this first procedure. In the second procedure, a nonlinear function is derived using the parameterized t-distribution density model. This nonlinear function is robust against the undue influence of outliers fundamentally. Moreover, the stability of the proposed algorithm and the robust property of misestimating the parameters (kurtosis) have been studied. By combining the t-distribution model with a family of light-tailed distributions (sub-Gaussian) model, we can separate the mixture of sub-Gaussian and super-Gaussian source components. Through the analysis of artificially synthesized data and real-world magnetoencephalographic (MEG) data, we illustrate the efficacy of this robust approach.

This paper presents a novel algorithm of spatial and temporal equalization for multiuser detection with an antenna array based on a robust subspace method and a self-adaptive step-size training technique. Our robust approach includes two procedures. In the first procedure, a robust subspace technique is utilized to reduce the high-power of additive noise and the correlation among the channels. Moreover, a cross-validation technique is applied to estimate the number of signals. In the second procedure, a self-adaptive algorithm for updating the stepsize coefficient is presented in order to identify a dynamic channel. The computer simulation results illustrate the effectiveness and performance of the proposed method.