A study on intention recognition based on analysis of human brain activity
| Project/Area Number |
23650538
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Educational technology
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| Research Institution | Kobe University |
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Principal Investigator |
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2012: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2011: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | ヒューマン・インターフェイス / コミュニケーション / ヒューマン・インターフェース |
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| Research Abstract |
In this study, we focused on human verbal communication based on analysis of human brain activity data obtained by magnetoencephalography (MEG). In 2011, we proposed a new weighting method using a multiple kernel learning (MKL) algorithm to localize the brain area contributing to the accurate vowel discrimination. Our MKL simultaneously estimates both the classification boundary and the weight of each MEG sensor. The estimated weight indicates how the corresponding sensor is useful for classifying the MEG response patterns. But our proposed method using multiple kernel learning had a high computational cost. In 2012, we proposed a novel and fast weighting method using an AdaBoost algorithm to find the sensor area contributing to the accurate discrimination of vowels. Then, in 2013, we proposed a random projection for feature extraction of human activity data.
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Report
(4 results)
Research Products
(6 results)
