Binge eating disorder is the most common eating disorder and therefore an important health problem worldwide often resulting in obesity. Current investigations on binge eating disorder’s impact on the human brain regarding electroencephalography data are limited to traditional approaches. In this study we make use of a Machine Learning method both for distinguishing individuals affected by a BED and healthy individuals with an overall accuracy of 81.25% and highlighting low theta sub-band in the range of 4.5 – 6 Hz as the most important distinctive feature. Individuals with a BED show significantly higher theta activity. Using Machine learning approaches based on EEG data is a promising approach in order to facilitate disorder identification and to provide novel insights for health scientists.
Raab, Dominik; Baumgartl, Hermann; and Buettner, Ricardo, "Machine Learning Based Diagnosis of Binge Eating Disorder Using EEG Recordings" (2020). PACIS 2020 Proceedings. 97.
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