Name
Abstract | ||
|---|---|---|
Recognition of epileptic electroencephalogram (EEG) signals using machine learning techniques is becoming popular. In general, the construction of intelligent epileptic EEG recognition system involves two steps. First, an appropriate feature extraction method is applied to obtain representative features from the original raw EEG signals. Second, an effective intelligent model is trained based on t... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TFUZZ.2016.2637405 | IEEE Transactions on Fuzzy Systems |
Keywords | Field | DocType |
Electroencephalography,Feature extraction,Brain modeling,Fuzzy systems,Data models,Learning systems,Collaborative work | Black box (phreaking),Weighting,Recognition system,Multiview learning,Feature extraction,Artificial intelligence,Eeg data,Fuzzy control system,Mathematics,Electroencephalography,Machine learning | Journal |
Volume | Issue | ISSN |
25 | 1 | 1063-6706 |
Citations | PageRank | References |
15 | 0.57 | 34 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yizhang Jiang | 1 | 382 | 27.24 |
| Zhaohong Deng | 2 | 647 | 35.34 |
| Fu Lai Chung | 3 | 1534 | 86.72 |
| guanjin wang | 4 | 52 | 5.73 |
| Pengjiang Qian | 5 | 133 | 11.25 |
| Kup-Sze Choi | 6 | 526 | 47.41 |
| Shitong Wang | 7 | 1485 | 109.13 |