Multi-segment majority voting decision fusion for MI EEG brain-computer interfacing

Padfield, Natasha and Ren, Jinchang and Murray, Paul and Qing, Chunmei and Zhao, Huimin and Zheng, Jiangbin (2021) Multi-segment majority voting decision fusion for MI EEG brain-computer interfacing. Cognitive Computation, 13 (6). pp. 1484-1495. ISSN 1866-9964

[thumbnail of Padfield-etal-CC-2021-Multi-segment-majority-voting-decision-fusion] Text. Filename: Padfield_etal_CC_2021_Multi_segment_majority_voting_decision_fusion.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 21 October 2022.

Download (836kB) | Request a copy


    Brain-computer interfaces (BCIs) based on the electroencephalogram (EEG) generated during motor imagery (MI) have the potential to be used in brain-controlled prosthetics, neurorehabilitation and gaming. Many MI EEG classification systems segment EEG into windows for classification. However, a comprehensive analysis of decision fusion based on the segmented EEG data, within the context of different classifiers, has not been carried out. This study presents a multi-segment majority voting (MSMV) decision fusion approach in which an EEG trial is segmented using overlapping windows. Segments are labelled and a final classification label for the trial is derived through majority voting, using the common spatial pattern (CSP) features. The impact of the MSMV approach on the classification accuracy of six classifiers was investigated. The effects of window size and overlap were analysed. Results were generated using five different subsets of EEG channels, and channel subsets for static EEG analysis are also proposed. The BCI Competition III dataset IVa was used. The MSMV decision fusion approach was found to significantly improve the classification accuracy for linear discriminant analysis (LDA), support vector machine (SVM), naïve-Bayes (NB) and random forest (RF) classifiers. The classification accuracy was improved by 5.02%, 4.41%, 1.25% and 3.62% for the SVM, LDA, NB and RF classifiers, respectively. The channel analysis indicated the importance of central-parietal and central-frontal electrode regions for MI EEG classification. MSMV decision fusion improved MI EEG classification performance and could be considered for future studies, particularly in online systems that deal with buffered data.

    ORCID iDs

    Padfield, Natasha, Ren, Jinchang, Murray, Paul ORCID logoORCID:, Qing, Chunmei, Zhao, Huimin and Zheng, Jiangbin;