CNN-based EEG Classification Method for Drug Use Detection

Common methods to detect whether a person uses drugs require taking biological samples of the subject, which have time limitation due to the samples. To avoid this, this paper proposes a CNN-based EEG classification method for drug use detection, which does not require taking biological samples of the subject and can trace a longer drug use history of the subject. In this paper, a convolutional neural network-based EEG classification algorithm incorporating batch normalization after the convolutional layer and also introducing dropout operation in the fully connected layer to speed up the training process is designed to distinguish between healthy controls and drug addicts, which reduces the sensitivity of parameters, effectively mitigates the occurrence of overfitting and improves the accuracy compared to traditional machine learning algorithms. Data were collected from eight healthy controls and eight drug addicts. The algorithm obtained the classification accuracy of 85.46% using eight-fold cross-validation. The result of classification shows that the method is an effective way to detect whether the examined person is drug addict, which can easier bring hidden drug addicts under control and reduce the social harm caused by drugs.