A. James Clark School of Engineering

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Subject: search.filters.subject.Electroencephalography

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    Analysis of Gamma-Band Auditory Responses in Schizophrenia
    (2015) Walsh, Benjamin Bryan; Simon, Jonathan Z; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Schizophrenia is a debilitating mental illness that affects 1% of the general population. One characteristic symptom is auditory hallucinations, which is experienced by almost all patients sometime in their lifetime. To investigate differences in auditory response in general, 50 schizophrenic patients and 50 age and sex-matched healthy controls were presented with auditory click trains at 40 Hz. Responses are recorded using electroencephalography (EEG). Magnitude and phase of responses at 40 Hz are computed using Gabor filters. Supporting previous literature, a significant difference in inter-trial phase coherence (ITC) and overall power is found between patients and controls, in particular near stimulus onset. Additionally, this study also investigated inter-subject phase coherence (ISC). This study finds that ISC is in fact higher for patients, in particular near stimulus onset. One possible explanation is that while healthy controls develop a preferred phase for perception, schizophrenic patients exhibit phase that is primarily stimulus-driven.
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    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.