Name
Abstract | ||
|---|---|---|
•Classification of normal and depression using EEG signals.•Employed a 13-layer deep convolutional neural network model.•Minimum hand-crafted features required in this work.•Obtained accuracy of 93.54% using the left hemisphere EEG data.•Obtained accuracy of 95.49% using the right hemisphere EEG data. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.cmpb.2018.04.012 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
Convolutional neural network,Deep learning,Depression,EEG,Electroencephalogram | Computer vision,Lateralization of brain function,Pattern recognition,Convolutional neural network,Computer science,Artificial intelligence,Classifier (linguistics),Artificial neural network,Electroencephalography,Right hemisphere | Journal |
Volume | ISSN | Citations |
161 | 0169-2607 | 28 |
PageRank | References | Authors |
1.02 | 40 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rajendra Acharya U | 1 | 4666 | 296.34 |
| Shu Lih Oh | 2 | 536 | 25.57 |
| Yuki Hagiwara | 3 | 641 | 29.34 |
| Jen-Hong Tan | 4 | 745 | 32.04 |
| Hojjat Adeli | 5 | 2150 | 148.37 |
| D. Puthankattil Subha | 6 | 87 | 4.81 |