TABLE 3.

Test accuracy when training individual participants separately.

	DT	RF	ADB	LR	MLP	SVM	KNN	LDA	LR-CNN	CNN	LSTM
Participant 1	0.819	0.931	0.932	0.825	0.936	0.931	0.906	0.931	0.951	0.989	0.959
Participant 2	0.826	0.933	0.933	0.933	0.934	0.936	0.919	0.933	0.942	0.978	0.968
Participant 3	0.838	0.932	0.932	0.932	0.933	0.934	0.931	0.932	0.929	0.992	0.962
Participant 4	0.828	0.935	0.935	0.935	0.935	0.935	0.928	0.935	0.946	0.992	0.972
Participant 5	0.816	0.933	0.933	0.933	0.933	0.936	0.915	0.933	0.919	0.984	0.954
Participant 6	0.767	0.934	0.934	0.934	0.934	0.935	0.917	0.933	0.956	0.977	0.961
Participant 7	0.820	0.935	0.936	0.935	0.935	0.936	0.915	0.835	0.927	0.969	0.959
Participant 8	0.788	0.932	0.932	0.932	0.932	0.932	0.920	0.932	0.933	0.996	0.967

DT, decision tree; RF, random forest; ADB, adboost; LR, logistic regression; MLP, multilayer perceptron; SVM, support vector machine; KNN, k-nearest neighbor; LDA, linear discriminant analysis; LR-CNN, logistic regression and convolutional neural network; CNN, convolutional neural network; LSTM, long short-term memory.