Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach

M Thilagaraj; S Ramkumar; N Arunkumar; A Durgadevi; K Karthikeyan; S Hariharasitaraman; M Pallikonda Rajasekaran; Petchinathan Govindan

doi:10.1155/2022/4487254

Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach

Comput Intell Neurosci. 2022 Feb 25:2022:4487254. doi: 10.1155/2022/4487254. eCollection 2022.

Authors

M Thilagaraj¹, S Ramkumar², N Arunkumar³, A Durgadevi⁴, K Karthikeyan⁵, S Hariharasitaraman⁶, M Pallikonda Rajasekaran⁷, Petchinathan Govindan⁸

Affiliations

¹ Department of Electronics and Instrumentation Engineering, Karpagam College of Engineering, Coimbatore, India.
² School of Computing, Kalasalingam Academy of Research and Education, Virudhunagar, India.
³ Department of Biomedical Engineering, Rathinam Technical Campus, Coimbatore, India.
⁴ Department of Electrical and Electronics Engineering, K. Ramakrishnan College of Engineering, Trichy, India.
⁵ Department of Electrical and Electronics Engineering, Ramco Institute of Technology, Rajapalayam, India.
⁶ School of Computer Science and Engineering, VIT Bhopal, Bhopal, Madhya Pradesh, India.
⁷ Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Virudhunagar, India.
⁸ Department of Electrical and Electronics Technology, Ethiopian Technical University, Addis Ababa, Ethiopia.

Abstract

Transforming human intentions into patterns to direct the devices connected externally without any body movements is called Brain-Computer Interface (BCI). It is specially designed for rehabilitation patients to overcome their disabilities. Electroencephalogram (EEG) signal is one of the famous tools to operate such devices. In this study, we planned to conduct our research with twenty subjects from different age groups from 20 to 28 and 29 to 40 using three-electrode systems to analyze the performance for developing a mobile robot for navigation using band power features and neural network architecture trained with a bioinspired algorithm. From the experiment, we recognized that the maximum classification performance was 94.66% for the young group and the minimum classification performance was 94.18% for the adult group. We conducted a recognizing accuracy test for the two contrasting age groups to interpret the individual performances. The study proved that the recognition accuracy was maximum for the young group and minimum for the adult group. Through the graphical user interface, we conducted an online test for the young and adult groups. From the online test, the same young-aged people performed highly and actively with an average accuracy of 94.00% compared with the adult people whose performance was 92.00%. From this experiment, we concluded that, due to the age factor, the signal generated by the subjects decreased slightly.

MeSH terms

Adult
Age Factors
Algorithms
Brain-Computer Interfaces*
Electroencephalography*
Humans
Machine Learning*
Neural Networks, Computer
User-Computer Interface
Young Adult