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Title: Movement-Imagery Brain-Computer Interface: EEG Classification of Beta Rhythm Synchronization Based on Cumulative Distribution Function
Authors: Sasayama, Teruyoshi
Kobayashi, Tetsuo  kyouindb  KAKEN_id
Keywords: electroencephalogram (EEG)
brain-machine interface (BCI)
event-related synchronization (ERS)
spline Laplacian
Hilbert transform
Issue Date: Dec-2011
Publisher: The Institute of Electronics, Information and Communication Engineers
Volume: E94D
Issue: 12
Start page: 2479
End page: 2486
Abstract: We developed a novel movement-imagery-based brain-computer interface (BCI) for untrained subjects without employing machine learning techniques. The development of BCI consisted of several steps. First, spline Laplacian analysis was performed. Next, time-frequency analysis was applied to determine the optimal frequency range and latencies of the electroencephalograms (EEGs). Finally, trials were classified as right or left based on β-band event-related synchronization using the cumulative distribution function of pretrigger EEG noise. To test the performance of the BCI, EEGs during the execution and imagination of right/left wrist-bending movements were measured from 63 locations over the entire scalp using eight healthy subjects. The highest classification accuracies were 84.4% and 77.8% for real movements and their imageries, respectively. The accuracy is significantly higher than that of previously reported machine-learning-based BCIs in the movement imagery task (paired t-test, p < 0.05). It has also been demonstrated that the highest accuracy was achieved even though subjects had never participated in movement imageries.
Rights: © 2011 The Institute of Electronics, Information and Communication Engineers
DOI(Published Version): 10.1587/transinf.e94.d.2479
Appears in Collections:Journal Articles

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