Research on drug addiction detection based on AR-TSNET with bimodal EEG-NIRS.

Traditional research on drug addiction assessment relies primarily on psychological scales, self-reports from drug users, and subjective judgments from doctors, ands lacks objective physiological indicators and quantitative evaluation. This study introduces a visual trigger paradigm designed to elicit drug cravings in individuals with substance addiction, employing Electroencephalogram (EEG) and Near-Infrared Spectroscopy (NIRS) for data acquisition. The dataset comprises recordings from 20 healthy individuals and 36 individuals with drug addiction. A deep learning algorithm named AR-TSNET, which utilizes feature-level fusion, is proposed to classify. The deep learning network uses two modules called Tception and Sception to process EEG and NIRS data. Tception extracts features from EEG data while Sception extracts features from NIRS data. Different attention mechanisms are incorporated to better align with the characteristics of the data. The attention mechanism assigns weights to features, reducing the interference of redundant features. Residual connections are utilized to address the issue of information loss caused by increased network depth, thereby enhancing the stability and robustness of the model. The classification accuracy achieved through k-fold cross-validation is 92.6%. The confusion matrix and ROC curve fully demonstrate the excellent performance of the model. A comparison of single-modal and bimodal evaluation metrics confirms the superior performance of bimodal data with higher information content. These results provide preliminary evidence that the proposed method is a promising and effective approach for assessing the severity of drug addiction. By leveraging advanced deep learning techniques, the method demonstrates not only high accuracy and reliability but also the potential for broader applications in addiction research and clinical practice. Furthermore, its straightforward implementation and objective nature offer valuable insights into addiction severity while reducing reliance on subjective assessments.