A Novel Transfer Enhanced -Expansion Move Learning Model for EEG Signals

Table 5

Comparison results of both synthetic and Bonn EEG datasets (the number in parentheses is the standard deviation).

Datasets (source data, target data, attributes, and classes)	Algorithms	Performance indices
		Synthetic dataset (240, 60, 2, 3)	EEM	0.8316 (0.0258)	0.7150 (0.1131)
Multiclass SVM	0.8513 (0.0214)		0.8523 (0.0812)
TSK-FS	0.8712 (0.0145)		0.8816 (0.0914)
TSC	0.88230.0313		0.92360.0158
TrEEM	0.8957 (0.0264)		0.9856 (0.0000)
Bonn EEG dataset (400, 100, 6, 5) (use KPCA to extract feature)	EEM	0.7754 (0.2146)	0.9800 (0.0000)
	Multiclass SVM	0.7827 (0.1834)	0.9643 (0.0023)
	TSK-FS	0.6819 (0.1579)	0.9623 (0.0000)
	TSC	0.72170.1241	0.9636 (0.0002)
	TrEEM	0.8323 (0.1652)	0.9600 (0.0012)
Bonn EEG dataset (400, 100, 6, 5) (use wavelet to extract feature)	EEM	0.7925 (0.0091)	0.7530 (0.0514)
	Multiclass SVM	0.7815 (0.0165)	0.9034 (0.0135)
	TSK-FS	0.7303 (0.0251)	0.9800 (0.0000)
	TSC	0.75010.1252	0.9600 (0.0021)
	TrEEM	0.8071 (0.0078)	0.9800 (0.0000)