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

Automatically detecting and removing Electroencephalogram (EEG) outliers is essential to design robust brain-computer interfaces (BCIs). In this paper, we propose a novel outlier detection method that works on the Riemannian manifold of sample covariance matrices (SCMs). Existing outlier detection methods run the risk of erroneously rejecting some samples as outliers, even if there is no outlier, due to the detection being based on a reference matrix and a threshold. To address this limitation, our method, Riemannian Spectral Clustering (RiSC), detects outliers by clustering SCMs into non-outliers and outliers, based on a proposed similarity measure. This considers the Riemannian geometry of the space and magnifies the similarity within the non-outlier cluster and weakens it between non-outlier and outlier clusters, instead of setting a threshold. To assess RiSC performance, we generated artificial EEG datasets contaminated by different outlier strengths and numbers. Comparing Hit-False (HF) difference between RiSC and existing outlier detection methods confirmed that RiSC could detect outliers significantly better (p < 0.001). In particular, RiSC improved HF difference the most for datasets with the most severe outlier contamination.

/pdf/detecting-eeg-outliers-for-bci-on-the-riemannian-manifold-49f8v8cdri.pdf

Detecting EEG outliers for BCI on the Riemannian manifold using spectral clustering

Complex sparse spatial filter for decoding mixed frequency and phase coded steady-state visually evoked potentials.

Design of Free Space Inverted U Shaped Antenna for ISM Band, GSM-900 and S-DMB Application

This paper presents a novel speech enhancement technique using spectral weighting with data adaptive postfiltering method. One disadvantage of spectral weighting method is to introduce musical noise. The empirical mode decomposition (EMD) based post filtering approach is implemented to reduce the speech distortion and musical noise effects for a wide range of signal-to-noise ratios (SNR). The EMD is used to discriminate the speech signal into high and low energy segments and then the weighting gains are adaptively smoothed based on the results of such discrimination. The gain modification is made in all frequency components to obtain the enhance speech signal. The experimental results show that proposed method is significantly more effective in speech enhancement compared to recently reported techniques.

Rabiul Islam

Papers

Detecting EEG outliers for BCI on the Riemannian manifold using spectral clustering

Complex sparse spatial filter for decoding mixed frequency and phase coded steady-state visually evoked potentials.

Design of Free Space Inverted U Shaped Antenna for ISM Band, GSM-900 and S-DMB Application

Speech enhancement using spectral weighting with EMD-based post filtering