Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

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Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Biometric recognition by gait: A survey of modalities and features

Motor Imagery Brain Computer Interface (MI-BCI) provides a non-muscular channel for communication to those who are suffering from neuronal disorders. The designing of an accurate and reliable MI-BCI system requires the extraction of informative and discriminative features. Common Spatial Pattern (CSP) has been potent and is widely used in BCI for extracting features in motor imagery tasks. The classifiers translate these features into device commands. Many classification algorithms have been devised, among those Support Vector Machine (SVM) and Linear Discriminate Analysis (LDA) have been widely used. In recent studies, the researchers are using deep neural networks for the classification of motor imagery tasks. This paper provides a comprehensive review of dominant feature extraction methods and classification algorithms in brain-computer interface for motor imagery tasks. Authors discuss existing challenges in the domain of motor imagery brain-computer interface and suggest possible research directions.

Signal processing techniques for motor imagery brain computer interface: A review

Objective Despite the vast research aimed at improving the performance of steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs), several limitations exist that restrict the use of such applications for long-term users in the real-world. One of the main challenges has been to reduce training time while maintaining good BCI performance. In view of this challenge, this survey identifies and compares the different training requirements of feature extraction methods for SSVEP-based BCIs. Approach This paper reviews the various state-of-the-art SSVEP feature extraction methods that have been developed and are most widely used in the literature. Main results The main contributions compared to existing reviews are the following: (i) a detailed summary, including a brief mathematical description of each feature extraction algorithm, providing a guide to the basic concepts of the state-of-the-art techniques for SSVEP-based BCIs found in literature; (ii) a categorisation of the training requirements of SSVEP-based methods into three categories, defined as training-free methods, subject-specific and subject-independent training methods; (iii) a comparative review of the training requirements of SSVEP feature extraction methods, providing a reference for future work on SSVEP-based BCIs. Significance This review highlights the strengths and weaknesses of the three categories of SSVEP training methods. Training-free systems are more practical but their performance is limited due to inter-subject variability resulting from the complex EEG activity. Feature extraction methods that incorporate some training data address this issue and in fact have outperformed training-free methods: subject-specific BCIs are tuned to the individual yielding the best performance at the cost of long, tiring training sessions making these methods unsuitable for everyday use; subject-independent BCIs that make use of training data from various subjects offer a good trade-off between training effort and performance, making these BCIs better suited for practical use.

https://iopscience.iop.org/article/10.1088/1741-2552/aaca6e/pdf

To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs.

Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra-class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait recognition datasets.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-bmt.2018.5063

Robust gait recognition: a comprehensive survey

OBJECTIVE When designing multiclass motor imagery-based brain-computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities associated with MI tasks. APPROACH A multichannel electroencephalogram (EEG) signal is decomposed into multiple subbands, and tangent features are then estimated on each subband. A mutual information analysis-based effective algorithm is implemented to select subbands containing features capable of improving motor imagery classification accuracy. Thus obtained features of selected subbands are combined to get feature space. A principal component analysis-based approach is employed to reduce the features dimension and then the classification is accomplished by a support vector machine (SVM). MAIN RESULTS Offline analysis demonstrates the proposed multiband tangent space mapping with subband selection (MTSMS) approach outperforms state-of-the-art methods. It acheives the highest average classification accuracy for all datasets (BCI competition dataset 2a, IIIa, IIIb, and dataset JK-HH1). SIGNIFICANCE The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.

https://iopscience.iop.org/article/10.1088/1741-2552/aac313/pdf

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.

Objective Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, as the number of commands increases. This paper develops a novel unsupervised method based on canonical correlation analysis (CCA) for accurate detection of stimulus frequency. Approach A novel unsupervised technique termed as binary subband CCA (BsCCA) is implemented in a multiband approach to enhance the frequency recognition performance of SSVEP. In BsCCA, two subbands are used and a CCA-based correlation coefficient is computed for the individual subbands. In addition, a reduced set of artificial reference signals is used to calculate CCA for the second subband. The analyzing SSVEP is decomposed into multiple subband and the BsCCA is implemented for each one. Then, the overall recognition score is determined by a weighted sum of the canonical correlation coefficients obtained from each band. Main results A 12-class SSVEP dataset (frequency range: 9.25-14.75 Hz with an interval of 0.5 Hz) for ten healthy subjects are used to evaluate the performance of the proposed method. The results suggest that BsCCA significantly improves the performance of SSVEP-based BCI compared to the state-of-the-art methods. The proposed method is an unsupervised approach with averaged information transfer rate (ITR) of 77.04 bits min-1 across 10 subjects. The maximum individual ITR is 107.55 bits min-1 for 12-class SSVEP dataset, whereas, the ITR of 69.29 and 69.44 bits min-1 are achieved with CCA and NCCA respectively. Significance The statistical test shows that the proposed unsupervised method significantly improves the performance of the SSVEP-based BCI. It can be usable in real world applications.

https://iopscience.iop.org/article/10.1088/1741-2552/aa5847/pdf

Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA.

Appearance based gait recognition becomes more difficult due to changing the gait styles by different cofactors like as cloths, carrying objects, view angles, surfaces and shoes. Out of others clothes is the most challenging issues in this area. Different part based approaches have been defined several effective and redundant body parts which can influence for individual recognition. In this paper we have study the gait by splitting it into very small window chunks and define a random window subspace method (RWSM) for clothing invariant Human gait recognition. Experiments are conducted on large-scale clothing variations OUR TEADMILL dataset B and shows superb performance than others classical gait recognition approaches.

Window based clothing invariant gait recognition

The tangent space mapping (TSM) becomes an effective method to implement brain computer interface (BCI) with motor imagery. In this paper, TSM is employed with multiband approach to extract discriminative features from electroencephalogram (EEG) to enhance classification accuracy. The EEG is decomposed into multiple subbands and the sample covariance matrices (SCMs) are then estimated on each of the subbands. Those matrices are then mapped onto the tangent space yielding the features. These obtained features of individual subbands are combined together. The dimension of the features space is reduced using principal component analysis (PCA) with one-way ANOVA. Support vector machine (SVM) based classification is performed employing the features with reduced dimension. The results of binary and four-class classification with public data sets showed that the proposed method significantly improved the performance compared to the state-of-the-art methods.

Classification of motor imagery BCI using multiband tangent space mapping

To assist disabled people by controlling an external system by using motor imagery (MI) is a common applications of brain computer interface (BCI) field. This paper we focused on an experimental comparison of covariance matrix averaging ways of EEG signal and EEG classification of two types of MI tasks $(right-hand^{\ast}$ foot and right-hand*left hand). Indeed averaging covariance matrices of EEG signal might be a used in brain computer interfaces (BCI) with common spatial pattern (CSP) method. Structured into trials is a usually paradigms of BCI which we have a tendency to use this structure into account. In addition, covariance matrices with non-Euclidean structure should be consideration likewise. We review much method for averaging covariance matrices in SVM from literature and observe through the experimented result using publicly available four datasets. Our experimental result show that for the case of averaging covariance matrices using Riemannian geometry with small dimension feature issue improve the classification performance. Our result shows the performance increase (2% >performance), but also the limit of this method once the increase feature dimension.

Rabiul Islam

Papers

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.

Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA.

Window based clothing invariant gait recognition

Classification of motor imagery BCI using multiband tangent space mapping

EEG Classification for MI-BCI using CSP with Averaging Covariance Matrices: An Experimental Study