Improving Automatic Recognition of Emotional States Using EEG Data Augmentation Techniques

• Published in: Procedia computer science Vol. 225; pp. 4225 - 4234
• Main Authors: Szczakowska, Patrycja, Wosiak, Agnieszka, żykwińska, Katarzyna
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2023
• Subjects: augmentation techniques; data preprocessing; EEG analysis; emotion recognition; Gaussian noise; data preprocessing; augmentation techniques; Gaussian noise; emotion recognition; EEG analysis
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2023.10.419

Emotion recognition is crucial for improving communication between humans and computers. Electroencephalography (EEG) signals can be used for this purpose, but the limited amount of available EEG data poses challenges in creating accurate classification models, particularly when using deep machine learning methods. To address this issue, we investigated the impact of data augmentation on the quality of emotion prediction for inter-subject classification based on public emotion EEG datasets, the MAHNOB. The effectiveness of different data augmentation techniques, including sliding windows of varying lengths, overlapping windows, and Gaussian noise, were tested. We verified the augmentation techniques by automated emotion classification using a shallow convolutional neural network and the Valence/Arousal model. The results demonstrate that data augmentation can significantly improve the accuracy of emotion prediction for individual subjects, with the noise method being the most effective. Augmentation using Gaussian noise achieves up to a 30% improvement for a single subject in both Arousal and Valence emotion model dimensions, compared to the baseline. Our findings highlight the potential of data augmentation as a promising approach for improving the accuracy of emotion recognition using EEG signals.