A CNN-TRANSFORMER-BASED BRAIN-COMPUTER INTERFACE MODEL FOR DIGITAL ART THERAPY IN DEPRESSION INTERVENTION AMONG COLLEGE STUDENTS

Targeting the depression-prone college student population, this study proposes an intelligent intervention system integrating brain–computer interface (BCI) technology with digital art therapy. Based on multi-channel electroencephalogram (EEG) signals, a hierarchical feature extraction and classification model is designed, consisting of a Time–Frequency Encoding module (TFE), a Multiscale Temporal Modeling module (MSTM), and an Emotion Discrimination module (EDM). The TFE module extracts local time–frequency features of EEG signals using multi-scale convolution and pooling structures. The MSTM module employs a dual-branch architecture combining coarse- and fine-grained convolutions with a Transformer mechanism to capture both local details and global temporal dependencies, achieving multiscale temporal feature modeling. The EDM module performs multi-class emotion classification using a fully connected network. Based on the recognized emotional states, an interactive digital art feedback module is developed, mapping real-time emotion states to artistic visual images to promote emotional regulation and self-expression. Experimental validation across multiple participants demonstrates that the proposed model achieves high accuracy and good cross-subject generalization in multi-class emotion recognition tasks. Moreover, the digital art feedback module shows a positive effect in improving participants’ psychological states. The proposed intervention framework combines real-time processing, hierarchical feature modeling, and cross-subject adaptability, offering a novel technological solution and application reference for digital mental health interventions in university settings.