Real-time analysis for brain activity measured by near-infrared spectroscopy considering path lengths
| Project/Area Number |
25420385
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Communication/Network engineering
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| Research Institution | Tokyo Denki University |
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Principal Investigator |
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| Research Collaborator |
SUGAI Masako
OGIHARA Hiroki
HAYAMI Kengo
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| Project Period (FY) |
2013-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2015: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2013: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 近赤外分光法 / 非線形時系列解析 / リカレンス・プロット / NIRS / 脳機能計測 / リカレンスプロット / 因果律 |
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| Outline of Final Research Achievements |
In this study, (1) we used unthresholded recurrence plots of near-infrared spectroscopy (NIRS) data to detect the difference in brain activity between task periods and non-task periods. Histograms derived from the recurrence plots showed a statistical difference between the periods. Throughout the pre-task period, task period, and post-task period, the histogram kept its shape statistically and shifted horizontally according to the period. Therefore, the changes in dynamical systems describing brain activity were observed as changes in histograms derived from NIRS data.
(2)We introduced the extreme learning machine (ELM) for the task-rest classification of brain activity measurement data by NIRS. (3)We analyzed NIRS data measured during a mental arithmetic task. We then estimated the Higuchi fractal dimension (HFD) from the NIRS time-series data.
More experiments of (2) and (3) are needed because the number of subject was small.
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Report
(5 results)
Research Products
(14 results)
