Movement-Imagery Brain-Computer Interface: EEG Classification of Beta Rhythm Synchronization Based on Cumulative Distribution Function

Teruyoshi SASAYAMA  Tetsuo KOBAYASHI  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.12   pp.2479-2486
Publication Date: 2011/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.2479
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Human-computer Interaction
electroencephalogram (EEG),  brain-machine interface (BCI),  event-related synchronization (ERS),  spline Laplacian,  Hilbert transform,  

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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.