Comprehensive Review on Eye Close and Eye Open Activities Using EEG in Brain–Computer Interface

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

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Signal processing algorithms for SSVEP-based brain computer interface: State-of-the-art and recent developments

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201280 ◽

2021 ◽

pp. 1-15

Author(s):

Jie Hong ◽

Xiansheng Qin

Keyword(s):

Signal Processing ◽

Feature Extraction ◽

State Of The Art ◽

Brain Computer Interface ◽

Interface State ◽

Computer Interface ◽

Comprehensive Review ◽

Recent Developments ◽

Signal Processing Algorithms ◽

Processing Algorithms

Over past two decades, steady-state evoked potentials (SSVEP)-based brain computer interface (BCI) systems have been extensively developed. As we all know, signal processing algorithms play an important role in this BCI. However, there is no comprehensive review of the latest development of signal processing algorithms for SSVEP-based BCI. By analyzing the papers published in authoritative journals in nearly five years, signal processing algorithms of preprocessing, feature extraction and classification modules are discussed in detail. In addition, other aspects existed in this BCI are mentioned. The following key problems are solved. (1) In recent years, which signal processing algorithms are frequently used in each module? (2) Which signal processing algorithms attract more attention in recent years? (3) Which modules are the key to signal processing in BCI field? This information is very important for choosing the appropriate algorithms, and can also be considered as a reference for further research. Simultaneously, we hope that this work can provide relevant BCI researchers with valuable information about the latest trends of signal processing algorithms for SSVEP-based BCI systems.

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A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Sensors ◽

10.3390/s21062173 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2173

Author(s):

Amardeep Singh ◽

Ali Abdul Hussain ◽

Sunil Lal ◽

Hans W. Guesgen

Keyword(s):

Motor Imagery ◽

Brain Computer Interface ◽

Computer Interface ◽

Comprehensive Review ◽

Current State ◽

Critical Issues ◽

Recent Developments ◽

Kinesthetic Imagination ◽

Number Of Publications ◽

Electroencephalogram Eeg

Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.

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