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

Sensors ◽  
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.

Motor Imagery Classification Using EEG Signals for Brain-Computer Interface Applications

2019 ◽  
pp. 241-251
Author(s):  
Subrota Mazumdar ◽  
Rohit Chaudhary ◽  
Suruchi Suruchi ◽  
Suman Mohanty ◽  
Divya Kumari ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Nearest Neighbor ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Test Cases ◽  
Multiple Channels ◽  
Eeg Signals ◽  
Brain Computer Interfacing ◽  
Electroencephalogram Eeg

In this chapter, a nearest neighbor (k-NN)-based method for efficient classification of motor imagery using EEG for brain-computer interfacing (BCI) applications has been proposed. Electroencephalogram (EEG) signals are obtained from multiple channels from brain. These EEG signals are taken as input features and given to the k-NN-based classifier to classify motor imagery. More specifically, the chapter gives an outline of the Berlin brain-computer interface that can be operated with minimal subject change. All the design and simulation works are carried out with MATLAB software. k-NN-based classifier is trained with data from continuous signals of EEG channels. After the network is trained, it is tested with various test cases. Performance of the network is checked in terms of percentage accuracy, which is found to be 99.25%. The result suggested that the proposed method is accurate for BCI applications.

Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain–Computer Interface

2019 ◽  
Vol 29 (01) ◽  
pp. 1850014 ◽  
Author(s):  
Marie-Constance Corsi ◽  
Mario Chavez ◽  
Denis Schwartz ◽  
Laurent Hugueville ◽  
Ankit N. Khambhati ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Performance Enhancement ◽  
Brain Computer Interface ◽  
Classification Performance ◽  
Computer Interface ◽  
Computer Interfaces ◽  
The Impact ◽  
Electroencephalogram Eeg ◽  
Fusion Approach ◽  
Single Modality

We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain–computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of noninvasive BCIs.

scholarly journals Reduce Calibration Time in Motor Imagery Using Spatially Regularized Symmetric Positives-Definite Matrices Based Classification

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 379 ◽  
Author(s):  
Amardeep Singh ◽  
Sunil Lal ◽  
Hans Guesgen
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Spatial Filter ◽  
Covariance Matrices ◽  
Small Sample ◽  
Computer Interface ◽  
Limited Applicability ◽  
Calibration Time ◽  
Electroencephalogram Eeg ◽  
New Framework

Electroencephalogram (EEG) based motor imagery brain–computer interface (BCI) requires large number of subject specific training trials to calibrate the system for a new subject. This results in long calibration time that limits the BCI usage in practice. One major challenge in the development of a brain–computer interface is to reduce calibration time or completely eliminate it. To address this problem, existing approaches use covariance matrices of electroencephalography (EEG) trials as descriptors for decoding BCI but do not consider the geometry of the covariance matrices, which lies in the space of Symmetric Positive Definite (SPD) matrices. This inevitably limits their performance. We focus on reducing calibration time by introducing SPD based classification approach. However, SPD-based classification has limited applicability in small training sets because the dimensionality of covariance matrices is large in proportion to the number of trials. To overcome this drawback, our paper proposes a new framework that transforms SPD matrices in lower dimension through spatial filter regularized by prior information of EEG channels. The efficacy of the proposed approach was validated on the small sample scenario through Dataset IVa from BCI Competition III. The proposed approach achieved mean accuracy of 86.13 % and mean kappa of 0.72 on Dataset IVa. The proposed method outperformed other approaches in existing studies on Dataset IVa. Finally, to ensure the robustness of the proposed method, we evaluated it on Dataset IIIa from BCI Competition III and Dataset IIa from BCI Competition IV. The proposed method achieved mean accuracy 92.22 % and 81.21 % on Dataset IIIa and Dataset IIa, respectively.

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

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.

scholarly journals Improving multi-class EEG-motor imagery classification using two-stage detection on one-versus-one approach

2020 ◽  
Vol 5 (2) ◽  
pp. 85-92
Author(s):  
Adi Wijaya ◽  
Teguh Bharata Adji ◽  
Noor Akhmad Setiawan
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Classification Performance ◽  
Kappa Coefficient ◽  
Computer Interface ◽  
Eeg Signal ◽  
Statistical Measure ◽  
Eeg Signals ◽  
Two Stage ◽  
Electroencephalogram Eeg

The multi-class motor imagery based on Electroencephalogram (EEG) signals in Brain-Computer Interface (BCI) systems still face challenges, such as inconsistent accuracy and low classification performance due to inter-subject dependent. Therefore, this study aims to improve multi-class EEG-motor imagery using two-stage detection and voting scheme on one-versus-one approach. The EEG signal used to carry out this research was extracted through a statistical measure of narrow window sliding. Furthermore, inter and cross-subject schemes were investigated on BCI competition IV-Dataset 2a to evaluate the effectiveness of the proposed method. The experimental results showed that the proposed method produced enhanced inter and cross-subject kappa coefficient values of 0.78 and 0.68, respectively, with a low standard deviation of 0.1 for both schemes. These results further indicated that the proposed method has an ability to address inter-subject dependent for promising and reliable BCI systems.

Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

2013 ◽  
Vol 133 (3) ◽  
pp. 635-641
Author(s):  
Genzo Naito ◽  
Lui Yoshida ◽  
Takashi Numata ◽  
Yutaro Ogawa ◽  
Kiyoshi Kotani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
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