Name
Abstract | ||
|---|---|---|
Brain-computer Interface (BCI) provides a direct communication pathway for the brain and the outward environment. Specifically, motor imagery-based BCIs (MI-BCIs) has the advantage of actively outputting instructions without any external stimuli. Although this paradigm has been investigated for many years, individual MI-BCI is still of low performance. To this end, a collaborative strategy was proposed for MI-BCI system in this study. A20-channel EEG was adopted to inspect the classification performances of collaborative and individual MI-BCI. For 8 healthy subjects, four different motor imagery mental tasks (both hands, feet, left hand and right hand) were tested. Experimental results showed that, compared with that of individual system, the performance of MI-BCI with multiuser collaborative strategy could be improved by 16.5%. The proposed collaborative strategy could provide an available approach for BCI modulation and neural feedback research. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICDSP.2018.8631864 | DSL |
Keywords | Field | DocType |
Collaboration,Electroencephalography,Task analysis,Band-pass filters,Matrix decomposition,Brain-computer interfaces,Covariance matrices | Computer vision,Task analysis,Computer science,Brain–computer interface,Matrix decomposition,Speech recognition,Artificial intelligence,Stimulus (physiology),Electroencephalography,Motor imagery | Conference |
ISSN | ISBN | Citations |
1546-1874 | 978-1-5386-6811-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yijie Zhou | 1 | 0 | 1.01 |
| Bin Gu | 2 | 1019 | 88.98 |
| Tingfei Dai | 3 | 0 | 0.34 |
| Zhongpeng Wang | 4 | 7 | 1.55 |
| Xizi Song | 5 | 0 | 4.73 |
| Minpeng Xu | 6 | 27 | 17.17 |
| Feng He | 7 | 0 | 0.34 |
| Dong Ming | 8 | 105 | 51.47 |