Molla, M.K.I.; Ahamed, S.; Almassri, A.M.M.; Wagatsuma, H. Classification of Motor Imagery Using Trial Extension in Spatial Domain with Rhythmic Components of EEG. Mathematics2023, 11, 3801.
Molla, M.K.I.; Ahamed, S.; Almassri, A.M.M.; Wagatsuma, H. Classification of Motor Imagery Using Trial Extension in Spatial Domain with Rhythmic Components of EEG. Mathematics 2023, 11, 3801.
Molla, M.K.I.; Ahamed, S.; Almassri, A.M.M.; Wagatsuma, H. Classification of Motor Imagery Using Trial Extension in Spatial Domain with Rhythmic Components of EEG. Mathematics2023, 11, 3801.
Molla, M.K.I.; Ahamed, S.; Almassri, A.M.M.; Wagatsuma, H. Classification of Motor Imagery Using Trial Extension in Spatial Domain with Rhythmic Components of EEG. Mathematics 2023, 11, 3801.
Abstract
A single paragraph of about 200 words maximum. Electroencephalography (EEG) accumulates the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imagery (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG is decomposed into a finite set of narrowband signals obtained from individual EEG channels using Fourier transformation based bandpass filter. Each of the subband signals represents narrowband rhythmic components which characterize the brain activities related to motor imagery. The subband signals are arranged to extent the dimension of EEG trial in spatial domain. The spatial features are extracted from the set of extended trials using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. Artificial neural network is used to classify MI tasks. The performance of the proposed method is evaluated using two publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.
Keywords
brain computer interface; classification; electroencephalography; motor imagery task; subband decomposition
Subject
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
