A Channel Selection Method for Emotion Recognition From EEG Based on Swarm-Intelligence Algorithms

Increasing demand for human-computer interaction applications has escalated the need for automatic emotion recognition as emotions are essential for natural communication. There are various information sources that can be used for recognizing emotions, such as speech, facial expressions, body movements, and physiological signals. Among those physiological signals are more reliable for better affective communication with machines since they are almost impossible to control. Therefore, automatic emotion recognition from EEG signals has been a topic intensely investigated. Emotions are experiences that arise various cognitive functions observed in different frequency bands involving multiple brain areas and recognition from EEG with high accuracies is only possible with a large number of features extracted from the whole brain in various bands. Emotion regulation also requires integration of cognitive functions and thus functional connectivity between regions should also be considered. In this paper, we extract 736 features based on spectral power and phase-locking values. We particularly focus on finding salient features for emotion recognition using swarm-intelligence (SI) algorithms. We applied well-known classification algorithms for recognizing positive and negative emotions using the feature sets that are selected by these algorithms. Besides, features that are selected by all of them commonly are used as a new feature set. We report accuracies between 56.27% and 60.29% on the average; noting that by decreasing the feature size by 87.17% (from 736 to 94.40) an average accuracy of 60.01± 8.93 was obtained with the random forest classifier. We also highlight the efficient electrode locations for emotion recognition. As a result, we define 11 channels as dominant and promising classification results are obtained.