Movement-imagery brain-computer interface: EEG classification of beta rhythm synchronization based on cumulative distribution function

Teruyoshi Sasayama, Tetsuo Kobayashi

Research output: Contribution to journalArticlepeer-review

Abstract

We developed a novel movement-imagery-based braincomputer 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, timefrequency 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.

Original languageEnglish
Pages (from-to)2479-2486
Number of pages8
JournalIEICE Transactions on Information and Systems
VolumeE94-D
Issue number12
DOIs
Publication statusPublished - Dec 2011

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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