| Project/Area Number |
09650477
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
計測・制御工学
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
MAEDA Makoto (1998-1999) Kyushu Institute of Technology, Faculty of Computer Science and Systems Engineering, Department of Control Engineering and Science, Research Associate, 情報工学部, 助手 (00274556)
井上 勝裕 (1997) 九州工業大学, 情報工学部, 助教授 (00150516)
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| Co-Investigator(Kenkyū-buntansha) |
INOUE Katsuhiro Kyushu Institute of Technology, Faculty of Computer Science and Systems Engineering, Department of Control Engineering and Science, Associate Professor, 情報工学部, 助教授 (00150516)
KUMAMARU Kousuke Kyushu Institute of Technology, Faculty of Computer Science and Systems Engineering, Department of Control Engineering and Science, Professor, 情報工学部, 教授 (30037949)
前田 誠 九州工業大学, 情報工学部, 助手 (00274556)
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| Project Period (FY) |
1997 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 1999: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1998: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1997: ¥2,000,000 (Direct Cost: ¥2,000,000)
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| Keywords | Electroencephalogram / Event Related Potential / Visual Evoked Response / Oddball Task / Identification / Signal Processing / Pattern Recognition / Human Interface / 選択反応実験 / 確率システム |
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| Research Abstract |
In this research project, we have performed studies on the development of detection method of brain activity under visual cognitive tasks based on information processing of electroencephalogram (EEG) signals and obtained following research results.
1. EEG signal processing methods
We have developed feature extraction and discrimination methods or EEG waves based on AR-model, Quasi ARMAX model and artificial neural networks. And it was also investigated that applied methods or wavelet analysis, blind separation analysis and event related de-synchronization analysis for EEG signals. An adaptive clustering method based on stochastic Newton method was developed and applied to EEG wave analysis. And it was confirmed that ERP (Event Related Potential) waves related to same kind of stimulus were belonged to some classes.
2. Visual stimulating period and effect of eye blink
Optimal visual stimulating period were about 500msec for the discrimination or ERP signals, And eye blinks generated the noise component of EEG waves similar to ERP in shape. The effects of eye blink for the discrimination of EEG waves are under investigation.
3. Discrimination of EEG waves under visual cognitive tasks
The effective electrode positions for the discrimination of ERP were Fz and Pz. Discrimination of stimuli (target or non-target) was tried based on EEG signal processing (AR model and statistical pattern recognition method). The accuracy based on regional EEG signals (spatial filtered signals) ws above 80%. These results suggest that human interface based on EEG signal processing would be feasible in near future.
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