IEICE TRANSACTIONS on Information
Movement-Imagery Brain-Computer Interface: EEG Classification of Beta Rhythm Synchronization Based on Cumulative Distribution Function
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.12 pp.2479-2486
- Publication Date
- 2011/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.2479
- Type of Manuscript
- PAPER
- Category
- Human-computer Interaction
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Keyword
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Teruyoshi SASAYAMA, Tetsuo KOBAYASHI, "Movement-Imagery Brain-Computer Interface: EEG Classification of Beta Rhythm Synchronization Based on Cumulative Distribution Function" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 12, pp. 2479-2486, December 2011, doi: 10.1587/transinf.E94.D.2479.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.2479/_p
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@ARTICLE{e94-d_12_2479,
author={Teruyoshi SASAYAMA, Tetsuo KOBAYASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Movement-Imagery Brain-Computer Interface: EEG Classification of Beta Rhythm Synchronization Based on Cumulative Distribution Function},
year={2011},
volume={E94-D},
number={12},
pages={2479-2486},
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.},
keywords={},
doi={10.1587/transinf.E94.D.2479},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Movement-Imagery Brain-Computer Interface: EEG Classification of Beta Rhythm Synchronization Based on Cumulative Distribution Function
T2 - IEICE TRANSACTIONS on Information
SP - 2479
EP - 2486
AU - Teruyoshi SASAYAMA
AU - Tetsuo KOBAYASHI
PY - 2011
DO - 10.1587/transinf.E94.D.2479
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2011
AB - 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.
ER -
English
