Name
Title | ||
|---|---|---|
Sparse learning of band power features with genetic channel selection for effective classification of EEG signals |
Abstract | ||
|---|---|---|
•Band power featured sparse learning for EEG data classification.•Genetic algorithm based channel selection within sparse learning.•Classification of motor imagery and idle state EEG data in three classes.•Significantly outperforming a few state-of-the-arts even some deep learning models.•More computationally efficient than classic classifiers such as SVM. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.08.067 | Neurocomputing |
Keywords | DocType | Volume |
Brain-computer interface (BCI),Motor imagery (MI) electroencephalography (EEG),Sparse learning (SL),Genetic algorithm (GA),Channel selection | Journal | 463 |
ISSN | Citations | PageRank |
0925-2312 | 1 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Natasha Padfield | 1 | 1 | 0.34 |
| Jinchang Ren | 2 | 1144 | 88.54 |
| Paul Murray | 3 | 1 | 0.68 |
| Huimin Zhao | 4 | 206 | 23.43 |