Multivariate EEG Signal Processing Techniques for the Aid of Severely Disabled People



Electroencephalography, Brain Computer Interface, Motor Imagery EEG, Cohen’s Kappa


Electroencephalography (EEG) has been used for several years as a trace of signals for facilitating subjects with serious infirmities to communicate with computers and other devices. Many studies have revealed the correlation of mental tasks with the EEG signals for actual or fictional movements. However, the performance of Brain Computer Interface (BCI) using EEG signal is still below enough to assist any disabled people. One reason could be that the researchers in this field (motor imagery based BCI) normally use two to three channels of EEG signal. This might limit the performance of BCI, as an extra source of information generally helps in detecting a person's motor movement intentions. Therefore, the proposed research work is involved with three or more channels of EEG signal for online BCI. Two fundamental objectives for BCI based on motor movement imagery from multichannel signals are aimed at in this research work: i) to develop a technique of multivariate feature extraction for motor imagery related to multichannel EEG signals; and ii) to develop an appropriate machine learning based feature classification algorithm for Brain Computer Interface. Nevertheless, all other problems like interfacing and real-time operations with current BCIs are also addressed and attempts are made to reduce the problems. The methodology can be described by following steps as follows: i) at least 3 channels of EEG signal are recorded; ii) a few features are extracted from preprocessed EEG signal; iii) all extracted features are classified to generate commands for BCI; iv) finally evaluate the performance of the proposed algorithm for BCI. The challenge of this research work is to investigate and find an appropriate model for online (real-time) BCI with a realistic performance to be made in achieving better lives for people with severe disabilities in Malaysia and abroad.


Download data is not yet available.


Metrics Loading ...


I. Lazarou, S. Nikolopoulos, P.C. Petrantonakis, I. Kompatsiaris and M. Tsolaki, “EEG-Based Brain–Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21st Century, Review Article,” Frontiers in Human Neuroscience, 12(14), pp. 1-18, 2018.

D.D. Chakladar and S. Chakraborty, “Feature Extraction and Classification in Brain-Computer Interfacing: Future Research Issues and Challenges,” Natural Computing for Unsupervised Learning, pp. 101-131, 2019.

M. Jafari, T. Aflalo, S. Chivukula, S.S. Kellis, M.A. Salas, S.L. Norman, K. Pejsa, C.Y. Liu and R.A. Andersen, “The Human Primary Somatosensory Cortex Encodes Imagined Movement in the Absence of Sensory Information,” Communications Biology, 3(757), pp. 1-7, 2020.

S.N. Abdulkader, A. Atia and M.S.M Mostafa, “Brain Computer Interfacing: Applications and Challenges,” Egyptian Informatics Journal, 16, pp. 213-230, 2015.

B. Blankertz, L. Acqualagna, S. Dahne, S. Haufe, M. Schultze-Kraft, I. Stur, M. Uscumlic, M.A. Wenzel, G. Curio and M. Klaus-Robert, “The Berlin Brain-Computer Interface: Progress Beyond Communication and Control,” Frontiers in Neuroscience, 10(530), pp. 1-24, 2016.

R.A. Ramadan, and A.V. Vasilakos, “Brain Computer Interface: Control Signals Review,” Neurocomputing, 223, pp. 26-44, 2017.

A. Fernández-Rodríguez, F. Velasco-Álvarez, and R. Ron-Angevin, “Review of Real Brain-Controlled Wheelchairs,” Journal of Neural Engineering, 13(6), 2016.

K.S. Hong, M.J. Khan and M.J. Hong, “Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces,” Frontiers in Human Neuroscience, 12(246), pp. 1-25, 2018.

K.S. Hong, and M.J. Khan, “Hybrid-BCI Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review,” Frontiers in Neurorobotics, 11(35), pp. 1-27, 2017.

J.B. Hyun, S.K. Hyun, A. Minkyu, C. Hohyun, and A. Sangtae, “Ergonomic Issues in Brain-Computer Interface Technologies: Current Status, Challenges, and Future Direction,” Computational Intelligence and Neuroscience, 2020(4876397), pp. 1-2, 2020.

K. Moonyoung, C. Hohyun, W. Kyungho, A. Minkyu and C.J. Sung, “Event-related Desynchronization (ERD) May Not be Correlated with Motor Imagery BCI Performance,” IEEE International Conference on Systems, Man, and Cybernetics, 2018.

M.H. Alomari, E. Awada and O. Younis, “Subject-Independent EEG-Based Discrimination Between Imagined and Executed, Right and Left Fists Movements,” European Journal of Scientific Research, 118(3), pp. 364-373, 2014.

H. Bashashati, R.K. Ward, G.E. Birch, A. Bashashati, “Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces,” PLoS One 10(6), pp. 1-12, 2015.

N.T.M. Huong, H.Q. Linh and L.Q. Khai, “Classification of Left/Right Hand Movement EEG Signals Using Event Related Potentials and Advanced Features,” IFMBE Proceedings, 63, pp. 209-, 2018.

M.R. Hasan, M.I. Ibrahimy, S.M.A. Motakabber and S. Shahid, “Classification of Multichannel EEG Signal by Linear Discriminant Analysis,” Advances in Intelligent Systems and Computing, 1089, pp. 279-282, 2015.




How to Cite

Ibrahimy, M. I. ., & Ibrahimy, A. I. . (2022). Multivariate EEG Signal Processing Techniques for the Aid of Severely Disabled People. Asian Journal of Electrical and Electronic Engineering, 2(1), 40–44. Retrieved from




Most read articles by the same author(s)