Name
Abstract | ||
|---|---|---|
How to select the appropriate frequency band to classify EEG signal by motor imagery is discussed in this paper. Our proposal is an improvement of the conventional Bayesian Spatio-Spectral Filter Optimization (BSSFO). Defect of BSSFO is on the way to generate the renewal particle of the filter bank, such a random number generation. To avoid a local optimum, an evolutional update method of particles is introduced. It is shown that performance of the EEG classification ability is improved. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.2991/jrnal.2016.2.4.3 | JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE |
Keywords | Field | DocType |
spatio-spectral filter,EEG,classification,optimization,mutual information,common spatial filter | Eeg classification,Pattern recognition,Computer science,Filter bank,Artificial intelligence,Mutual information,Electroencephalography | Journal |
Volume | Issue | ISSN |
2 | 4 | 2352-6386 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masanao Obayashi | 1 | 198 | 26.10 |
| Takuya Geshi | 2 | 0 | 0.34 |
| Takashi Kuremoto | 3 | 196 | 27.73 |
| Shingo Mabu | 4 | 493 | 77.00 |