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• W2067622862 abstract "While invasively recorded brain activity is known to provide detailed information on motor commands, it is an open question at what level of detail information about positions of body parts can be decoded from non-invasively acquired signals. In this work it is shown that index finger positions can be differentiated from non-invasive electroencephalographic (EEG) recordings in healthy human subjects. Using a leave-one-subject-out cross-validation procedure, a random forest distinguished different index finger positions on a numerical keyboard above chance-level accuracy. Among the different spectral features investigated, high $beta$-power (20-30 Hz) over contralateral sensorimotor cortex carried most information about finger position. Thus, these findings indicate that finger position is in principle decodable from non-invasive features of brain activity that generalize across individuals." @default.
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• W2067622862 date "2014-05-01" @default.
• W2067622862 modified "2023-09-25" @default.
• W2067622862 title "Decoding index finger position from EEG using random forests" @default.
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• W2067622862 doi "https://doi.org/10.1109/cip.2014.6844513" @default.
• W2067622862 hasPublicationYear "2014" @default.
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