Name
Abstract | ||
|---|---|---|
•We devised a novel model, KNN-based smooth auto-encoder, to achieve accurate recognition of motor imaging EEG signals.•K-SAE construct a new input and learns the robust features representation by reconstructing this new input instead of the original input, which is different from the traditional automatic encoder (AE).•The Gaussian filter is selected as the convolution kernel function in k-SAE to smooth the noise in the feature.•The experiments in this paper select two sets of data for verifying the validity of the proposed method. One is obtained by EEG signal acquisition experiment and the other is public data set. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.artmed.2019.101747 | Artificial Intelligence in Medicine |
Keywords | Field | DocType |
KNN-based smooth auto-encoder,BCI,Motor imagery,Feature extraction,EEG recognition | Gaussian filter,k-nearest neighbors algorithm,Data mining,Data set,Autoencoder,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Encoder,Kernel (image processing),Motor imagery | Journal |
Volume | ISSN | Citations |
101 | 0933-3657 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianlun Tang | 1 | 7 | 4.86 |
| Wang Ting | 2 | 55 | 14.14 |
| Yiming Du | 3 | 1 | 0.35 |
| Yuyan Dai | 4 | 1 | 0.35 |