Incorporating Deep Learning and Error Potential Feedback to a BMI System to Enhance User Experience
| Project/Area Number |
15H06922
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Single-year Grants |
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| Research Field |
Brain biometrics
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| Research Institution | Advanced Telecommunications Research Institute International |
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Principal Investigator |
Penaloza Christian 株式会社国際電気通信基礎技術研究所, 石黒浩特別研究所, 研究員 (80753532)
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| Project Period (FY) |
2015-08-28 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | BCI / Brain Computer Interface / Android / Motor Imagery / BMI / Deep Learning / Brain Machine Interface / Error Potential / Robot Learning |
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| Outline of Final Research Achievements |
A Brain Machine Interface (BMI) system that integrates an Android robot was sucessfully developed. The android robot provided realistic visual feedback to the user so that he/she could concentrate better and modulate his/her brain activity. A new training protocol that addresses the deficiencies of the classical approach and takes advantage of body-abled user capabilities was proposed. Experimental results suggest that android feedback based BCI training improves the modulation of sensorimotor rhythms during motor imagery task. Moreover, we discovered that the influence of body ownership transfer illusion towards the android induced thrhough a haptic interface might have an effect in the modulation of event related desynchronization/synchronization (ERD/ERS) activity.
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Report
(3 results)
Research Products
(3 results)
