EEG Signals and Spectrogram with Deep Learning Approaches Emotion Analysis with Images

Abstract

EEG signals are one of the most basic methods used in identifying and analyzing brain activities. Visual representation of EEG signals can be achieved with spectrograms. Spectrograms represent a visual representation of a signal's signal strength over time. In this study, the signals in an EEG dataset containing ‘positive’, ‘negative’ and ‘neutral’ emotion classes were classified with a deep learning model, and then these signals were transformed into a spectrogram image in the dataset with convolutional network model and also with transfer learning (EfficientNet and XceptionNet). Multiple classification was performed with pre-trained models. The success value obtained by the classification of the EEG signals and the success of the visualization in this classification were measured and presented by comparison. While higher accuracy values were achieved in the classification of signals with the deep network model, in metrics such as precision and F1-score, the classification of images with the proposed convolutional network model achieved much higher performance.