REVIEW ARTICLE


Brain-Computer Interface for Persons with Motor Disabilities - A Review



T. Anitha1, *
iD
, N. Shanthi1
iD
, R. Sathiyasheelan2
iD
, G. Emayavaramban3
iD
, T. Rajendran4
iD

1 Department of Biochemistry, Biotechnology & Bioinformatics, Avinashilingam Institute for Home Science & Higher Education for Women (Deemed to be University), India
2 Riga Technical University, Riga, Latvia
3 Department of Electrical and Electronics Engineering, Karpagam Academy of Higher Education (Deemed to be University), India.
4 Makeit Technologies, Coimbatore, Tamil Nadu, India


© 2019 Anitha et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Biochemistry, Biotechnology & Bioinformatics, Avinashilingam Institute for Home Science & Higher Education for Women (Deemed to be University), India; E-mail: anitha.bioinformatics@gmail.com


Abstract

Aim /Objective:

A Brain-Computer Interface (BCI) is a communication medium, which restructures brain signals into respective commands for an external device.

Methodology:

A BCI allows its target users like persons with motor disabilities to act on their environment using brain signals without using peripheral nerves or muscles. In this review article, we have presented a view on different BCIs for humans with motor disabilities.

Results & Conclusion:

From the study, it is clear that the P300 based Electroencephalography (EEG)BCIs with Steady-State Visually Evoked Potential (SSVEP) non-parametric feature extraction techniques work with high efficiency in the major parameters like Information Bit Transfer Rate (ITR), Mutual Information (MI) rate and Low Signal to Noise Ratio (SNR) and achieve a maximum classification accuracy using Self Organized Fuzzy Neural Network (SOFNN).

Keywords: Brain-Computer Interface (BCI), Non parametric Statistical Features, P300, Electroencephalography (EEG), Steady-State Visually Evoked Potential (SSVEP), non-parametric statistical feature extraction, Information Bit Transfer Rate (ITR), Mutual Information (MI), Signal to Noise Ratio (SNR) and Self Organized Fuzzy Neural Network (SOFNN).