| Summary: | 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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