| Project/Area Number |
26350993
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Brain biometrics
|
|---|
| Research Institution | Nagoya University |
|---|
Principal Investigator |
Bagarinao Epifanio 名古屋大学, 脳とこころの研究センター, 特任准教授 (00443218)
|
|---|
| Co-Investigator(Renkei-kenkyūsha) |
ISODA Haruo 名古屋大学, 脳とこころの研究センター, 教授 (40223060)
|
|---|
| Project Period (FY) |
2014-04-01 – 2017-03-31
|
| Project Status |
Completed (Fiscal Year 2016)
|
| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2016: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2015: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2014: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
|
|---|
| Keywords | real-time functional MRI / neurofeedback / support vector machine / brain machine interface / machine learning |
|---|
| Outline of Final Research Achievements |
In this study, a real-time fMRI-based brain state decoder system to identify different brain states, viewed as brain activity pattern, was developed and used to investigate whether participants can control their brain activity pattern to match a pre-determined target pattern using neurofeedback. The system attained an overall processing time that was faster than the image acquisition time set at 2s. Using support vector machines, brain states associated with three tasks (imagined tapping, word generation, and serial subtraction tasks) were successfully reproduced as evidenced by the consistently high mean classification accuracy of greater than 80% during feedback scans.
|
