Obsessive-compulsive disorder detection using ensemble of scalp EEG-based convolutional neural network

Obsessive-compulsive disorder (OCD) causes unwanted thoughts and repetitive actions and leads to many problems in a person's life. In this study, Electroencephalography (EEG) signals and deep learning methods were used to diagnose OCD patients early. Three popular pre-trained convolutional neural network (CNN) models are developed for scalp-EEG data analysis: EEGNet, Shallow ConvNet, and Deep ConvNet. Three pre-trained CNNs were utilized as transfer learning models. Following the fine-tuning of models with our raw EEG data, an ensemble of three scalp EEG-based CNN models was used, employing weighted majority voting, in which weights of these base classifiers were optimized by the Differential Evolution (DE) algorithm. Shallow ConvNet has the highest performance with an accuracy of 85.91±0.72, sensitivity of 82.19±0.72, and specificity of 93.34±2.91 among all models. Ensemble these three scalp EEG-based CNN models achieved superior performance with an accuracy of 87.03±0.46, sensitivity of 82.21±0.56, and specificity of 96.69±1.28. Consequently, a hybrid proposed model based on pre-treatment raw EEG signals can independently extract distinctive characteristics and accurately identify OCD patients.