Impact of depressed state on attention and language processing during news broadcasts: EEG analysis and machine learning approach

Abstract

Abstract While information enriches daily life, it can also sometimes have a negative impact, depending on an individual’s mental state. We recorded electroencephalogram (EEG) signals from depressed and non-depressed individuals classified based on the Beck Depression Inventory-II score while they listened to news to clarify differences in their attention to affective information and the impact of attentional bias on language processing. Results showed that depressed individuals are characterized by delayed attention to positive news and require a more increased load on language processing. The feasibility of detecting a depressed state using these EEG characteristics was evaluated by classifying individuals as depressed and non-depressed individuals. The area under the curve in the models trained by the EEG features used was 0.73. This result shows that individuals’ mental states may be assessed based on EEG measured during daily activities like listening to news.