Rapid Detection Technology for Cocaine and Methamphetamine Acute Abuse based on EEG

Abstract Multi-domain characterization and a new fast detection method for acute illegal psychostimulants abuse detection based on waking-state EEG of mice is proposed in this paper. To get corftical electroencephalogram (EEG), three groups of mice were injected with cocaine (Coca), methamphetamine (Meth), and saline (Sali) respectively following the experimental paradigm of drug abuse. The EEG features were extracted out by multi-domain views, including time, frequency, complexity, dynamics, and independent domains to obtain acute drug abuse effects on the brain. New strategy combing time domain with frequency domain is developed as multi-domain input and by means of dimension transformation approach preserving temporal information, the performance of drug abuse detection is greatly improved with deep learning models of ResNet50. Results show that comparing to support vector machine (SVM), k nearest neighbor (kNN), random forest (RF), and long short-term memory fully convolutional networks (LSTM-FCN), Res-Net50 based on our proposed multi-domain features has best F1-score of 95.10%. This promising method provides a low-cost, fast, and widely assisted technology for psychostimulants abuse identification.