Name
Abstract | ||
|---|---|---|
Brain-computer interface (BCI) has facilitated communication for people who cannot move their bodies. BCI system requires time-consuming calibration phase to make reasonable classifier. To reduce the calibration phase, it is natural to attempt to make cross-subject classifier using other subjects' data. However, electroencephalogram (EEG) data are notably varied over subjects, that is, subject-specific. Thus, it is challenging to make subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigated subject-independent motor imagery BCI performance with selective subjects (choosing subjects yielding reasonable performance selectively) instead of using all available subjects. We observed from MI-BCI dataset including 52 subjects that selective subject pooling strategy worked reasonably. Finally, criterion of selection of subjects for subject-independent BCI was suggested. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/BCI51272.2021.9385292 | 2021 9th International Winter Conference on Brain-Computer Interface (BCI) |
Keywords | DocType | ISSN |
Brain-computer interfaces,Electroencephalography,Calibration | Conference | 2572-7680 |
ISBN | Citations | PageRank |
978-1-7281-8485-2 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyungho Won | 1 | 0 | 1.69 |
| Moonyoung Kwon | 2 | 0 | 2.37 |
| Minkyu Ahn | 3 | 0 | 0.34 |
| Sung Chan Jun | 4 | 0 | 0.68 |