GameEmo-CapsNet: Emotion Recognition from Single-Channel EEG Signals Using the 1D Capsule Networks

Abstract

Human emotion recognition with machine learning methods through electroencephalographic (EEG) signals has become a highly interesting subject for researchers. Although it is simple to define emotions that can be expressed physically such as speech, facial expressions, and gestures, it is more difficult to define psychological emotions that are expressed internally. The most important stimuli in revealing inner emotions are aural and visual stimuli. In this study, EEG signals using both aural and visual stimuli were examined and emotions were evaluated in both binary and multi-class emotion recognitions models. A general emotion recognition model was proposed for non-subject-based classification. Unlike in previous studies, a subject-based testing was performed for the first time in the literature. Capsule Networks, a new neural network model, has been developed for binary and multi-class emotion recognition. In the proposed method, a novel fusion strategy was introduced for binary-class emotion recognition and the model was tested using the GAMEEMO dataset. Binary-class emotion recognition achieved a classification accuracy which was 10% better than the classification performance achieved in other studies in the literature. Based on these findings, we suggest that the proposed method will bring a different perspective to emotion recognition.

EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features

“…Compared with traditional machine learning models, deep neural networks show a more efficient performance [ 26 , 27 , 28 , 29 ]. They can not only automatically extract effective features, but also mark key frequency bands and brain regions.…”

“…First, the time domain methods focus on the EEG signals' temporal information, including the typical features of Hjorth parameters, fractal dimensional features, and higher-order crossover features. Second, the frequency domain methods often convert the collected EEG signals (0-50 Hz) into five sub-bands (i.e., delta (1-4 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (31-50 Hz)) [22] and extract features, such as power spectral density, differential entropy and asymmetry, and rational asymmetry in different frequency bands [15]. Meanwhile, the time-frequency domain method combines the characteristics of both time and frequency domains, converting the EEG signals into sub-bands and using the windowing method for emotion classification.…”

Classification of Emotional and Immersive Outcomes in the Context of Virtual Reality Scene Interactions

“…In this study, the objective EEG method was used. EEG studies for emotion-state predictions have been widely used [27,28]. The EEG method has received more attention for examining brain dynamics during emotional tasks [29,30].…”