Abstract
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.
Highlights
The increasing role of technology and machines in human life makes it necessary to strengthen the interaction between humans and machines
Since emotions are extremely important in social interactions in daily life, emotion recognition systems are essential for affective human-machine interaction
As a contribution of the study, we evaluate the effectiveness of a new feature set containing only the features that are selected by all SI algorithms we used
Summary
The increasing role of technology and machines in human life makes it necessary to strengthen the interaction between humans and machines. Emotion is mostly defined as an experience that is associated with psychological phenomena. Emotions might last for a long time or might emerge for a very short time period. They are mostly accompanied by physiological reactions and physical responses. There exists several studies focusing on emotion recognition systems based on speech [1]–[4], facial expressions [5]–[7] and physiological signals [8]–[15]. The main issue is to find emotional salient features from several sources, analyzing feature sets [16], [17] to eliminate the irrelevant/unnecessary features and developing new classification frameworks to improve accuracies of existing classifiers [3], [18]. This study focuses on emotion recognition from EEG using band powers and phase-locking values as features
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.