Abstract
Electroencephalogram (EEG) signals have shown to be a good source of information for emotion recognition algorithms in Human-Brain interaction applications. In this paper, a reproducible framework is proposed for classifying human emotions based on EEG signals. The framework consists of extracting frequency-dependent features from raw EEG signals to form a three-dimensional EEG image which is classified by a convolutional neural network (CNN). The framework is used to show that the 3D input CNN outperforms conventional methods with two-dimensional input, using a public dataset. The implementation of the framework is publicly available to facilitate further work on this topic: https://github.com/KvanNoord/3D-CNN-EEG-Emotion-Classification.
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