Recognition of Affective States via Electroencephalogram Analysis and Classification
Understanding and reacting to the affective state of users is increasingly becoming important in the field of human–computer interaction (HCI) research and practice. Recent developments in brain–computer interface (BCI) technology has facilitated improved accuracy in human emotion detection and classification. In this paper, we investigate the possibility of using electroencephalogram (EEG) for the detection of four affective states based on a dimensional model (valence and arousal) of emotions. We conduct rigorous offline analysis for investigating the deep neural network (DNN) classification method in emotion detection. We also compare our classification performance with a random forest (RF) classifier and support vector machine (SVM). The data analysis results revealed that the proposed DNN-based classifier method outperformed the methods based on the SVM and RF classifiers.
KeywordsElectroencephalogram (EEG) Brain–Computer Interface (BCI) Emotion recognition Affective state DEAP dataset
The study was supported by the Human–Computer Interaction (HCI) Lab at King Abdulaziz City for Science and Technology in Riyadh, Saudi Arabia.
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