EEG Based Aptitude Detection System for Stress Regulation in Health Care Workers

Table 2

Literature review.

Ref	Modality signal	Features	Classification	Emotions	Accuracy (%)
[11]	EEG	Energy, entropy	SVM, KNN	Arousal, valence	86
[13]	EEG	Min, Max peak, power	LSTM	Arousal, valence, and liking	87
[14]	EEG	Min, Max peak, power	ANN	Stress, normal	60
[20]	EMG, ECG, RSP	Statistical, energy, entropy	LDA	Joy, anger, sadness, and pleasure	95
[24]	BVP, EMG, EDA, RSP	Statistical features	SVM, Fisher LDA	Amusement, contentment, disgust, fear, sadness, and neutral	92
[26]	EMG, EDA, ECG	No specific feature	No specific classifier	Arousal, valence	NA
[27]	EEG	Statistical features	SVM, ANN	Positive, negative, and neutral	91
[33]	EEG, EMG, Temp, GSR, RSP	Different features	MESAE	Arousal, valence	77
[34]	EEG	No specific features	LDA	Arousal, valence	87
[35]	EEG	DE, PSD	SVM	Negative, positive, and neutral	91.5
[36]	EEG	Spatial, spectral, temporal	CNN	Depression	86
[37]	EDA, HR, EMG	No specific features	HMM	Arousal, valence	81
[38]	EEG	Average PSD, mean, variance, Shannon’s entropy, zero crossing	LSSVM	Joy, peace, anger, and depression	65
[39]	EDA, HR	No specific features	Fuzzy logic	Stress	99.5
[40]	EEG	No specific features	Correlation analysis	Neutral, anger, sadness, anxiety, disgust, and surprise	90

Nomenclature for signal modalities: RSP denotes relative spectral power, EEG denotes electroencephalogram, ECG denotes electrocardiogram, GSR denotes galvanic skin response, EDA denotes electrodermal activity, BVP denotes blood volume pulse, HR/HP denotes heart rate/pulse, and Temp denotes temperature. Nomenclature for classifiers: LDA denotes latent discriminant analysis, KNN denotes K-nearest neighbors, ANN denotes artificial neural network, SVM denotes support vector machine, HMM denotes hidden Markov model, LSTM denotes long-short-term memory, DFA denotes deterministic finite automata, MESAE denotes multiple fusion layer based-ensemble classifier of stacked autoencoder, and MEMD denotes multiencoder to multidecoder.