EEG-Based Identification of Adolescent Non-Suicidal Self-Injury and Neurophysiological Interpretation Using an Explainable Deep Learning Framework

Non-suicidal self-injury (NSSI) among adolescents is a prevalent mental health problem and an important indicator of potential suicide risk. Early objective identification and neural mechanism analysis are therefore crucial for clinical screening and intervention. Traditional assessments mainly rely on self-report scales and clinical interviews, which are vulnerable to subjective bias, clinical experience, and missed diagnosis. Electroencephalography (EEG), with its non-invasive, low-cost, and high-temporal-resolution characteristics, provides a promising physiological basis for identifying NSSI-related neural abnormalities. However, EEG-based intelligent recognition of adolescent NSSI remains limited, and existing studies often emphasize classification performance while lacking systematic neurophysiological interpretation. To address these issues, this study proposes CGA-NSSI, a lightweight deep learning framework for adolescent NSSI recognition. The model integrates a one-dimensional convolutional neural network, bidirectional gated recurrent unit, and multi-head self-attention mechanism to extract local spatiotemporal EEG features, model long-range temporal dependencies, and focus on key pathology-related time segments and channels. A standardized preprocessing pipeline, together with Mixup augmentation and Focal Loss, is further used to alleviate sample imbalance and improve robustness in small clinical EEG datasets. Experiments on a real-world adolescent clinical EEG dataset show that CGA-NSSI can effectively identify NSSI-related EEG patterns under imbalanced sample conditions. Interpretability and functional connectivity analyses further reveal prefrontal-centered cross-regional network reorganization, excessive static functional coupling, reduced dynamic connectivity fluctuations, and increased abnormal state occupancy. These findings suggest that CGA-NSSI not only improves objective NSSI recognition but also provides neurophysiological evidence for understanding adolescent self-injury.