DEAP DIVE: Dataset Investigation with Vision transformers for EEG evaluation

• URL: http://arxiv.org/abs/2510.00725v1
• Date: Wed, 01 Oct 2025 10:07:07 GMT
• Title: DEAP DIVE: Dataset Investigation with Vision transformers for EEG evaluation
• Authors: Annemarie Hoffsommer, Helen Schneider, Svetlana Pavlitska, J. Marius Zöllner,
• Abstract summary: Accurately predicting emotions from brain signals has the potential to achieve goals such as improving mental health, human-computer interaction, and affective computing.<n>This work examines how subsets of EEG channels can be used for sufficiently accurate emotion prediction with low-cost EEG devices.
• Score: 11.8905212108669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately predicting emotions from brain signals has the potential to achieve goals such as improving mental health, human-computer interaction, and affective computing. Emotion prediction through neural signals offers a promising alternative to traditional methods, such as self-assessment and facial expression analysis, which can be subjective or ambiguous. Measurements of the brain activity via electroencephalogram (EEG) provides a more direct and unbiased data source. However, conducting a full EEG is a complex, resource-intensive process, leading to the rise of low-cost EEG devices with simplified measurement capabilities. This work examines how subsets of EEG channels from the DEAP dataset can be used for sufficiently accurate emotion prediction with low-cost EEG devices, rather than fully equipped EEG-measurements. Using Continuous Wavelet Transformation to convert EEG data into scaleograms, we trained a vision transformer (ViT) model for emotion classification. The model achieved over 91,57% accuracy in predicting 4 quadrants (high/low per arousal and valence) with only 12 measuring points (also referred to as channels). Our work shows clearly, that a significant reduction of input channels yields high results compared to state-of-the-art results of 96,9% with 32 channels. Training scripts to reproduce our code can be found here: https://gitlab.kit.edu/kit/aifb/ATKS/public/AutoSMiLeS/DEAP-DIVE.

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