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.

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    INTEGRATION OF INTRA-AUDITORY MODALITIES FOR THE ENHANCEMENT OF MOTOR PERFORMANCE AND COORDINATION IN A CONSTANT FORCE PRODUCTION TASK
    (2015) Koh, Kyung; Shim, Jae Kun; Kinesiology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    One of most fundamental problems in the field of neuromechanics is to understand how the central nervous system (CNS) integrates multiple sources of sensory information and coordinates multiple effectors in human movement. Much attention has been directed to the integration of multiple modalities between sensory organs (e.g., visual and auditory, visual and tactile, or visual and proprioceptor), while little is known about the integration of multiple modalities within one sensory (i.e., intra-sensory integration), especially regarding the auditory sensory. This dissertation investigated the mechanisms of intra-auditory integration for the control of multiple fingers during constant force production tasks, specifically regarding how the CNS utilizes multiple sources in auditory feedback, how the CNS deals with uncertainty in auditory feedback, and how the CNS adapts or learns a motor task using auditory feedback. The specific aims of this dissertation included: 1) development of analytical tools for the quantification of motor performance and coordination in a hierarchical structure of motor variability; 2) investigation into the effect of intra-auditory integration on motor performance and coordination (Experiment I); 3) investigation of the role of uncertainty in auditory information on the effectiveness of intra-auditory integration in motor performance and coordination (Experiment II); and 4) investigation of the auditory-motor learning in the context of motor performance and coordination (Experiment III). Results from Experiments I & II have indicated that the CNS can integrate frequency and intensity of auditory information to enhance motor performance and coordination among fingers. Intra-auditory integration was found to be most effective when uncertainty in auditory feedback was moderate between two extreme levels of uncertainty (low and high uncertainty). Results from Experiment III indicate that practice leads to the enhancement of performance by reducing individual finger variability without changes in inter-finger coordination. Further, the enhancement of performance and coordination after practice was specific to the intra-auditory modality that was available during practice. This dissertation discusses the mechanisms responsible for the changes in motor performance and coordination with auditory feedback and directions for future research are suggested.
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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.