Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

Browse

Filters

20111

Settings

Author: search.filters.author.Paek, Andrew YoungSubject: search.filters.subject.Finger

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Decoding Repetitive Finger Movements with Brain Signals Acquired Via Noninvasive Electroencephalography
    (2011) Paek, Andrew Young; Contreras-Vidal, Jose L; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    We 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.