Name
Title | ||
|---|---|---|
Temporal-spatial-frequency depth extraction of brain-computer interface based on mental tasks |
Abstract | ||
|---|---|---|
•Series and parallel structures are proposed by combining CNN and LSTM.•Temporal-spatial-frequency features of EEG are extracted at the same time.•Filters of the convolution layer are visualizd to interpret and understand CNN.•The series structure with compact CNN obtains the best result. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.bspc.2020.101845 | Biomedical Signal Processing and Control |
Keywords | Field | DocType |
Brain-computer interface (BCI),Electroencephalogram (EEG),Temporal-spatial-frequency,Convolutional neural network (CNN),Long short term memory (LSTM) | Data set,Pattern recognition,Convolutional neural network,Brain–computer interface,Artificial intelligence,Eeg data,Artificial neural network,Spatial frequency,Electroencephalography,Mathematics,Motor imagery | Journal |
Volume | ISSN | Citations |
58 | 1746-8094 | 1 |
PageRank | References | Authors |
0.41 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Wang | 1 | 250 | 56.88 |
| Weijian Huang | 2 | 1 | 0.41 |
| yang zhao | 3 | 35 | 20.16 |
| Chun Zhang | 4 | 1 | 0.41 |