Name
Title | ||
|---|---|---|
Toward Open-World Electroencephalogram Decoding Via Deep Learning: A comprehensive survey |
Abstract | ||
|---|---|---|
Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on noninvasively measured brain activity. Traditional EEG decoding methods have achieved moderate success when applied to data acquired in static, well-controlled lab environments. However, an open-world environment is a more realistic setting, where situations affecting ... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/MSP.2021.3134629 | IEEE Signal Processing Magazine |
DocType | Volume | Issue |
Journal | 39 | 2 |
ISSN | Citations | PageRank |
1053-5888 | 1 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xun Chen | 1 | 458 | 52.73 |
| chang li | 2 | 282 | 19.50 |
| Aiping Liu | 3 | 72 | 10.58 |
| Martin J. McKeown | 4 | 1 | 0.35 |
| Ruobing Qian | 5 | 1 | 0.35 |
| Z. Jane Wang | 6 | 406 | 55.43 |