Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition

Table 4

Details from comparison studies.

Studies	Features	Classifiers	Evaluation methods	Accuracy
				Arousal	Valence
Koelstra et al. [42]	CNS feature-based single modality	Gaussian Naive Bayes	Leave-one-trial-out validation	0.6200	0.5760
Chen et al. [46]	Time and frequency-domain features	Decision trees	10-fold cross-validation	0.6909	0.6789
Xu and Plataniotis [47]	Narrow-band spectral features	DBN	Leave-one-subject-out validation	0.6988	0.6688
Zhuang et al. [48]	The first difference in time series, etc.	SVM	Leave-one-trial-out validation for each subject	0.7199	0.6910
Huang et al. [49]	Autoregressive coefficients features	SVM	10-fold cross-validation	0.6730	0.6430
Li et al. [50]	Linear and nonlinear features	SVM	Leave-one-subject-out validation	-	0.5906
Chen at al [51]	Raw EEG features	H-ATT-BGRU	Leave-one-subject-out validation	0.6650	0.6790
The proposed method	MIC-based features	SVM	10-fold cross-validation	0.7185	0.7021