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Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing.

Jin, Jing and Liu, Chang and Daly, Ian and Miao, Yangyang and Li, Shurui and Wang, Xingyu and Cichocki, Andrzej (2020) 'Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing.' IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (10). pp. 2153-2163. ISSN 1534-4320

Bispectrum-Based_Channel_Selection_for_Motor_Imagery_Based_Brain-Computer_Interfacing.pdf - Accepted Version

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The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05).

Item Type: Article
Uncontrolled Keywords: Brain; Humans; Electroencephalography; Imagination; Computers; Signal Processing, Computer-Assisted; Brain-Computer Interfaces
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 14 Jul 2021 10:10
Last Modified: 15 Jan 2022 01:35

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