Decoding Arousal and Valence from Continuous MEG Data during Video Watching with Machine Learning

Mental disorders represent a prevalent and rising global health condition that is often closely associated with certain emotional states. Identification of emotional states based on brain function would have important consequences for mental illness diagnosis and personalized treatments. The curre...

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Päätekijä: Aimysheva, Arna
Muut tekijät: Kasvatustieteiden ja psykologian tiedekunta, Faculty of Education and Psychology, Psykologian laitos, Department of Psychology, Jyväskylän yliopisto, University of Jyväskylä
Aineistotyyppi: Pro gradu
Kieli:eng
Julkaistu: 2025
Aiheet:
Linkit: https://jyx.jyu.fi/handle/123456789/103690
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author Aimysheva, Arna
author2 Kasvatustieteiden ja psykologian tiedekunta Faculty of Education and Psychology Psykologian laitos Department of Psychology Jyväskylän yliopisto University of Jyväskylä
author_facet Aimysheva, Arna Kasvatustieteiden ja psykologian tiedekunta Faculty of Education and Psychology Psykologian laitos Department of Psychology Jyväskylän yliopisto University of Jyväskylä Aimysheva, Arna Kasvatustieteiden ja psykologian tiedekunta Faculty of Education and Psychology Psykologian laitos Department of Psychology Jyväskylän yliopisto University of Jyväskylä
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description Mental disorders represent a prevalent and rising global health condition that is often closely associated with certain emotional states. Identification of emotional states based on brain function would have important consequences for mental illness diagnosis and personalized treatments. The current thesis investigates the categorization of emotional states and specifically arousal and valence levels based on the XGBoost machine learning model used on frequency-domain MEG data. Emotion-inducing one minute video clips were shown to participants while MEG signals were recorded. After each video the participants evaluated their arousal and valence levels, which were later binarized based on the mean arousal and valence ratings. The XGBoost classifier achieved 0.86 and 0.78 classification accuracies for valence and arousal, respectively. Feature importance analysis agreed with previous findings, showing the importance of beta band activity with respect to arousal and alpha band activity with respect to valence. These results contribute to the understanding of emotion decoding from brain activity and demonstrate the potential of machine learning techniques in affective neuroscience. This combination provides potential applications for improving mental health diagnostics and therapeutic strategies.
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spellingShingle Aimysheva, Arna Decoding Arousal and Valence from Continuous MEG Data during Video Watching with Machine Learning emotional states arousal valence XGBoost Psykologia Psychology tunteet koneoppiminen MEG aivot
title Decoding Arousal and Valence from Continuous MEG Data during Video Watching with Machine Learning
title_full Decoding Arousal and Valence from Continuous MEG Data during Video Watching with Machine Learning
title_fullStr Decoding Arousal and Valence from Continuous MEG Data during Video Watching with Machine Learning Decoding Arousal and Valence from Continuous MEG Data during Video Watching with Machine Learning
title_full_unstemmed Decoding Arousal and Valence from Continuous MEG Data during Video Watching with Machine Learning Decoding Arousal and Valence from Continuous MEG Data during Video Watching with Machine Learning
title_short Decoding Arousal and Valence from Continuous MEG Data during Video Watching with Machine Learning
title_sort decoding arousal and valence from continuous meg data during video watching with machine learning
title_txtP Decoding Arousal and Valence from Continuous MEG Data during Video Watching with Machine Learning
topic emotional states arousal valence XGBoost Psykologia Psychology tunteet koneoppiminen MEG aivot
topic_facet MEG Psychology Psykologia XGBoost aivot arousal emotional states koneoppiminen tunteet valence
url https://jyx.jyu.fi/handle/123456789/103690 http://www.urn.fi/URN:NBN:fi:jyu-202506185487
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