Classification of Motor Imagery Using Trial Extension in Spatial Domain with Rhythmic Components of EEG

Md. Khademul Islam Molla (), Sakir Ahamed, Ahmed M. M. Almassri and Hiroaki Wagatsuma
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Md. Khademul Islam Molla: Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh
Sakir Ahamed: Department of Computer Science and Engineering, Varendra University, Rajshahi 6204, Bangladesh
Ahmed M. M. Almassri: Department of Intelligent Robotics, Faculty of Engineering, Toyama Prefectural University, Toyama 939-0398, Japan
Hiroaki Wagatsuma: Department of Human Intelligence Systems, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka 808-0196, Japan

Mathematics, 2023, vol. 11, issue 17, 1-18

Abstract: Electrical activities of the human brain can be recorded with electroencephalography (EEG). To characterize motor imagery (MI) tasks for brain–computer interface (BCI) implementation is an easy and cost-effective tool. The MI task is represented by a short-time trial of multichannel EEG. In this paper, the signal of each channel of raw EEG is decomposed into a finite set of narrowband signals using a Fourier-transformation-based bandpass filter. Rhythmic components of EEG are represented by each of the narrowband signals that characterize the brain activities related to MI tasks. The subband signals are arranged to extend the dimension of the EEG trial in the spatial domain. The spatial features are extracted from the set of extended trials using a common spatial pattern (CSP). An optimum number of features are employed to classify the motor imagery tasks using an artificial neural network. An integrated approach with full-band and narrowband signals is implemented to derive discriminative features for MI classification. In addition, the subject-dependent parameter optimization scheme enhances the performance of the proposed method. The performance evaluation of the proposed method is obtained using two publicly available benchmark datasets (Dataset I and Dataset II). The experimental results in terms of classification accuracy (93.88% with Dataset I and 91.55% with Dataset II) show that it performs better than the recently developed algorithms. The enhanced MI classification accuracy is very much applicable in BCI implementation.

Keywords: brain–computer interface; classification; electroencephalography; motor imagery task; subband decomposition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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