Research Article
Emotion Recognition Based on Framework of BADEBA-SVM
Table 3
Comparison with related work using EEG signals in DEAP dataset.
| Authors | # Channel | Approach in the model | Accuracy(%) |
| Chung et al. [21] | 32 | Power spectral analysis with Bayes classifier | 66.6(valence,2-class) | 32 | 53.4(valence,3-class) | Yoon et al. [22] | 32 | FFT enhanced feature extraction and classification | 70.9(valence,2-class) | 32 | 70.1(arousal,2-class) | Dai et al. [23] | 5 | Sparse constrained differential evolution enabled hybrid selection | 73.0(valence,2-class) | 5 | 74.5(arousal,2-class) | Our research | 8 | BADEBA optimal channel and classification | 74.86(valence,2-class) | 7 | 75.61(arousal,2-class) |
|
|