Enhanced Classification of Individual Finger Movements with ECoG

Motor decoding at the level of individual finger movements is critical for high-performance brain-machine interface (BMI) applications. In this work, we propose to exploit the temporal dynamics of the multi-channel electrocorticography (ECoG) signal from human subjects and modern machine learning al...

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Bibliographic Details
Published inConference record - Asilomar Conference on Signals, Systems, & Computers pp. 2063 - 2066
Main Authors Yao, Lin, Shoaran, Mahsa
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2019
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ISSN2576-2303
DOI10.1109/IEEECONF44664.2019.9048649

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Summary:Motor decoding at the level of individual finger movements is critical for high-performance brain-machine interface (BMI) applications. In this work, we propose to exploit the temporal dynamics of the multi-channel electrocorticography (ECoG) signal from human subjects and modern machine learning algorithms to improve the finger-level movement classification accuracy. Using a decision tree ensemble as the classifier and the temporally-concatenated features of ECoG as input, we achieved an average classification accuracy of 71.3%±7.1% on 3 subjects, 6.3% better than the state-of-the-art approach based on conditional random fields (CRF) on the same dataset. Our proposed method could enable a high-performance and minimally invasive cortical BMI for paralyzed patients.
ISSN:2576-2303
DOI:10.1109/IEEECONF44664.2019.9048649