EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Table 6

Comparison of classifiers used for emotion classification and its performance.

Research author	Classifiers	Best performance achieved	Intersubject or Intrasubject
[110]	Dynamical graph convolutional neural network	90.40%	Intrasubject and intersubject
[140]	Support vector machine	80.76%	Intrasubject and intersubject
[93]	Random forest, instance-based	98.20%	Intrasubject
[118]	Support vector machine	—	Intrasubject
[99]	Multilayer perceptron	76.81%	Intrasubject
[117]	K-nearest neighbor	95.00%	Intersubject
[92]	Support vector machine	73.10%	Intersubject
[104]	Support vector machine, K-nearest neighbor, convolutional neural network, deep neural network	82.81%	Intersubject
[141]	Support vector machine	81.33%	Intersubject
[102]	Support vector machine, convolutional neural network	81.14%	Intersubject
[103]	Gradient boosting decision tree	75.18%	Intersubject
[113]	Support vector machine	70.00%	Intersubject
[100]	Support vector machine	70.52%	Intersubject
[107]	Support vector machine, naïve Bayes	61.00%	Intersubject
[142]	Support vector machine	57.00%	Intersubject
[94]	Support vector machine, K-nearest neighbor	—	Intersubject
[111]	Support vector machine, K-nearest neighbor	98.37%	—
[143]	Convolutional neural network	97.69%	—
[144]	Support vector machine, backpropagation neural network, late fusion method	92.23%	—
[145]	Fisherface	91.00%	—
[93]	Haar, Fisherface	91.00%	—
[106]	Extreme learning machine	87.10%	—
[112]	K-nearest neighbor, support vector machine, multilayer perceptron	86.27%	—
[97]	Support vector machine, K-nearest neighbor, fuzzy networks, Bayes, linear discriminant analysis	83.00%	—
[105]	Naïve Bayes, support vector machine, K-means, hierarchical clustering	78.06%	—
[130]	Support vector machine, naïve Bayes, multilayer perceptron	71.42%	—
[95]	Gaussian process	71.30%	—
[96]	Naïve Bayes	68.00%	—