An Instrument for Measuring Depression From EEG Signals Using Attention-Enabled Residual-Based Convolutional Neural Network

Major depressive disorder remains a critical psychiatric condition, necessitating objective diagnostic instruments beyond conventional self-reported assessments. Electroencephalogram (EEG)-based deep learning (DL) approaches provide a non-invasive and data-driven solution. Yet, prior models have been limited in generalizability due to evaluation on a narrow set of datasets, and their lack of interpretability restricts clinical applicability. This paper presents AttResNet, a compact and efficient DL-based measurement instrument for electrical EEG signal processing and classification. In contrast to earlier approaches, which focus on algorithmic accuracy, AttResNet is developed as a measurement system optimized for embedded platforms. The model exhibits low computational complexity with 2.02 M parameters, requiring only 8 MB of storage, and is successfully implemented on a raspberry pi 4, where it demonstrates an inference time of 0.0059 s. Rigorous evaluations on three independent EEG datasets validate its robustness, achieving mean subject-independent classification accuracies of 99.7%, 96.7%, and 99.9%, surpassing existing approaches while ensuring generalizability. AttResNet enhances the design and development of biomedical instrumentation for mental health monitoring by combining modules for optimal signal measurement, feature extraction, and decision-making. Its implementation on embedded systems enables real-time EEG-based depression detection, marking a significant step toward scalable, wearable, and accessible instrumentation for mental health diagnostics in the internet of things era.