Decoding Repetitive Finger Movements with Brain Signals Acquired Via Noninvasive Electroencephalography

dc.contributor.advisorContreras-Vidal, Jose Len_US
dc.contributor.authorPaek, Andrew Youngen_US
dc.contributor.departmentBioengineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2011-10-08T06:21:10Z
dc.date.available2011-10-08T06:21:10Z
dc.date.issued2011en_US
dc.description.abstractWe investigated how well finger movements can be decoded from electroencephalography (EEG) signals. 18 hand joint angles were measured simultaneously with 64-channel EEG while subjects performed a repetitive finger tapping task. A linear decoder with memory was used to predict continuous index finger angular velocities from EEG signals. A genetic algorithm was used to select EEG channels across temporal lags between the EEG and kinematics recordings, which optimized decoding accuracies. To evaluate the accuracy of the decoder, the Pearson's correlation coefficient (r) between the observed and predicted trajectories was calculated in a 10-fold cross-validation scheme. Our results (median r = .403, maximum r = .704), compare favorably with previous studies that used electrocorticography (ECoG) to decode finger movements. The decoder used in this study can be used for future brain machine interfaces, where individuals can control peripheral devices through EEG signals.en_US
dc.identifier.urihttp://hdl.handle.net/1903/12028
dc.subject.pqcontrolledNeurosciencesen_US
dc.subject.pqcontrolledBiomedical engineeringen_US
dc.subject.pqcontrolledKinesiologyen_US
dc.subject.pquncontrolledBrain Machine Interfaceen_US
dc.subject.pquncontrolledDecodingen_US
dc.subject.pquncontrolledElectroencephalographyen_US
dc.subject.pquncontrolledFingeren_US
dc.subject.pquncontrolledHanden_US
dc.subject.pquncontrolledNoninvasiveen_US
dc.titleDecoding Repetitive Finger Movements with Brain Signals Acquired Via Noninvasive Electroencephalographyen_US
dc.typeThesisen_US

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