Name
Title | ||
|---|---|---|
Analysis and Classification for EEG Patterns of Force Motor Imagery Using Movement Related Cortical Potentials |
Abstract | ||
|---|---|---|
Motor imagery-based BCIs are the most natural human-computer interaction paradigms. In recent years, researchers have tried to decode the kinetic information of motor imagery. In this paper, we analyzed and discriminated the EEG patterns of different force levels motor imagery using MRCPs. In the experiment, nine healthy subjects were required to perform the hand force motor imagery tasks (30% MVC and 10% MVC). From the view of MRCPs, the most significant discrimination between the two levels of mental tasks was the manifestation of motor planning. The average classification accuracy for features involving both MRCP and CSP was 78.3%, which was 8.5% higher than the CSP-based features (p<;0.001) and 2% higher than the MRCP-based features. The results demonstrated the feasibility of using MRCPs for hand force motor imagery classification. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8512184 | 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Keywords | Field | DocType |
Brain-Computer Interfaces,Electroencephalography,Humans,Imagery, Psychotherapy,Imagination,Movement | Computer vision,Pattern recognition,Task analysis,Motor planning,Computer science,Feature extraction,Artificial intelligence,Electroencephalography,Motor imagery | Conference |
Volume | ISSN | ISBN |
2018 | 1557-170X | 978-1-5386-3647-3 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Wang | 1 | 150 | 45.41 |
| Minpeng Xu | 2 | 27 | 17.17 |
| Shan-Shan Zhang | 3 | 25 | 1.84 |
| yufeng ke | 4 | 1 | 7.78 |
| Dong Ming | 5 | 105 | 51.47 |