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Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification.

Miao, Yangyang and Jin, Jing and Daly, Ian and Zuo, Cili and Wang, Xingyu and Cichocki, Andrzej and Jung, Tzyy-Ping (2021) 'Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification.' IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29. 699 - 707. ISSN 1534-4320

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Abstract

The common spatial patterns (CSP) algorithm is the most popular spatial filtering method applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-computer interface (BCI) systems. The effectiveness of the CSP algorithm depends on optimal selection of the frequency band and time window from the EEG. Many algorithms have been designed to optimize frequency band selection for CSP, while few algorithms seek to optimize the time window. This study proposes a novel framework, termed common time-frequency-spatial patterns (CTFSP), to extract sparse CSP features from multi-band filtered EEG data in multiple time windows. Specifically, the whole MI period is first segmented into multiple subseries using a sliding time window approach. Then, sparse CSP features are extracted from multiple frequency bands in each time window. Finally, multiple support vector machine (SVM) classifiers with the Radial Basis Function (RBF) kernel are trained to identify the MI tasks and the voting result of these classifiers determines the final output of the BCI. This study applies the proposed CTFSP algorithm to three public EEG datasets (BCI competition III dataset IVa, BCI competition III dataset IIIa, and BCI competition IV dataset 1) to validate its effectiveness, compared against several other state-of-the-art methods. The experimental results demonstrate that the proposed algorithm is a promising candidate for improving the performance of MI-BCI systems.

Item Type: Article
Uncontrolled Keywords: Common spatial patterns (CSP), motor imagery (MI), electroencephalogram (EEG), brain-computer interface (BCI)
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 27 May 2021 13:11
Last Modified: 27 May 2021 13:11
URI: http://repository.essex.ac.uk/id/eprint/30505

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