{"ID":2861997,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00725","arxiv_id":"2510.00725","title":"DEAP DIVE: Dataset Investigation with Vision transformers for EEG evaluation","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.","short_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 analys...","url_abs":"https://arxiv.org/abs/2510.00725","url_pdf":"https://arxiv.org/pdf/2510.00725v1","authors":"[\"Annemarie Hoffsommer\",\"Helen Schneider\",\"Svetlana Pavlitska\",\"J. Marius Zöllner\"]","published":"2025-10-01T10:07:07Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","project_urls":"[\"https://gitlab.kit.edu/kit/aifb/ATKS/public/AutoSMiLeS/DEAP-DIVE\"]","has_code":false,"code_links":[{"ID":608864,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2861997,"paper_url":"https://arxiv.org/abs/2510.00725","paper_title":"DEAP DIVE: Dataset Investigation with Vision transformers for EEG evaluation","repo_url":"https://github.com/whatwg/fetch","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}