Name
Abstract | ||
|---|---|---|
Image decoding using electroencephalogram (EEG) has became a new topic for brain-computer interface (BCI) studies in recent years. Previous studies often tried to decode EEG signals modulated by a picture of complex object. However, it's still unclear how a simple image with different positions and orientations influence the EEG signals. To this end, this study used a same white bar with eight different spatial patterns as visual stimuli. Convolutional neural network (CNN) combined with long short-term memory (LSTM) was employed to decode the corresponding EEG signals. Four subjects were recruited in this study. As a result, the highest binary classification accuracy could reach 97.2% 95.7%, 90.2%, and 88.3% for the four subjects, respectively. Almost all subjects could achieve more than 70% for 4-class classification. The results demonstrate basic graphic shapes are decodable from EEG signals, which hold promise for image decoding of EEG-based BCIs. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/EMBC46164.2021.9630661 | 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) |
DocType | Volume | ISSN |
Conference | 2021 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingjuan Qiao | 1 | 0 | 0.34 |
| Jiabei Tang | 2 | 1 | 3.42 |
| Jiajia Yang | 3 | 0 | 0.34 |
| Minpeng Xu | 4 | 27 | 17.17 |
| Dong Ming | 5 | 105 | 51.47 |