Improving pre-movement pattern detection with filter bank selection.

Autor:	Jia H; Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, Catalonia, Spain., Sun Z; Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Saitama, Japan., Duan F; Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, College of Artificial Intelligence, Nankai University, Tianjin, People's Republic of China., Zhang Y; Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, United States of America.; Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, 18015, United States of America., Caiafa CF; Instituto Argentino de Radioastronomía, CONICET CCT La Plata/CIC-PBA/UNLP, V. Elisa, Argentina., Solé-Casals J; Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, Catalonia, Spain.; Department of Psychiatry, University of Cambridge, Cambridge CB2 3EB, United Kingdom.
Jazyk:	angličtina
Zdroj:	Journal of neural engineering [J Neural Eng] 2022 Nov 16; Vol. 19 (6). Date of Electronic Publication: 2022 Nov 16.
DOI:	10.1088/1741-2552/ac9e75
Abstrakt:	Objective . Pre-movement decoding plays an important role in detecting the onsets of actions using low-frequency electroencephalography (EEG) signals before the movement of an upper limb. In this work, a binary classification method is proposed between two different states. Approach . The proposed method, referred to as filter bank standard task-related component analysis (FBTRCA), is to incorporate filter bank selection into the standard task-related component analysis (STRCA) method. In FBTRCA, the EEG signals are first divided into multiple sub-bands which start at specific fixed frequencies and end frequencies that follow in an arithmetic sequence. The STRCA method is then applied to the EEG signals in these bands to extract CCPs. The minimum redundancy maximum relevance feature selection method is used to select essential features from these correlation patterns in all sub-bands. Finally, the selected features are classified using the binary support vector machine classifier. A convolutional neural network (CNN) is an alternative approach to select canonical correlation patterns. Main Results . Three methods were evaluated using EEG signals in the time window from 2 s before the movement onset to 1 s after the movement onset. In the binary classification between a movement state and the resting state, the FBTRCA achieved an average accuracy of 0.8968 ± 0.0847 while the accuracies of STRCA and CNN were 0.8228 ± 0.1149 and 0.8828 ± 0.0917, respectively. In the binary classification between two actions, the accuracies of STRCA, CNN, and FBTRCA were 0.6611 ± 0.1432, 0.6993 ± 0.1271, 0.7178 ± 0.1274, respectively. Feature selection using filter banks, as in FBTRCA, produces comparable results to STRCA. Significance . The proposed method provides a way to select filter banks in pre-movement decoding, and thus it improves the classification performance. The improved pre-movement decoding of single upper limb movements is expected to provide people with severe motor disabilities with a more natural, non-invasive control of their external devices. (© 2022 IOP Publishing Ltd.)
Databáze:	MEDLINE
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