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- W3013945861 abstract "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." @default.
- W3013945861 created "2020-04-03" @default.
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- W3013945861 date "2019-11-01" @default.
- W3013945861 modified "2023-09-25" @default.
- W3013945861 title "Enhanced Classification of Individual Finger Movements with ECoG" @default.
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- W3013945861 doi "https://doi.org/10.1109/ieeeconf44664.2019.9048649" @default.
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