Name
Abstract | ||
|---|---|---|
•A novel subject-based dipole selection method named PRDS is proposed for decoding motor imagery tasks in the source space.•PRDS includes two steps: the preliminary selection of dipoles with the data-driven method and the refinement of dipoles based on continuous wavelet transform.•The dipoles selected by PRDS are fully activated and can best reflect the differences among the multiclass MI-tasks, meanwhile have good adaptability for different subjects.•The wavelet coefficient power of the selected dipoles are input to the OVO–CSP to extract the fusion features of time-frequency-spatial domains, thus yielding better decoding performance and calculation efficiency for the four classes of MI-tasks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2020.03.055 | Neurocomputing |
Keywords | DocType | Volume |
MI-tasks decoding,EEG source imaging,Dipole selection,Common spatial patterns,Continuous wavelet transform | Journal | 402 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingai Li | 1 | 34 | 8.97 |
| Yu-xin Dong | 2 | 0 | 0.34 |
| Yanjun Sun | 3 | 10 | 1.88 |
| Jinfu Yang | 4 | 34 | 9.24 |
| Lijuan Duan | 5 | 1 | 2.72 |