GENet: A Generic Neural Network for Detecting Various Neurological Disorders From EEG

The global health burden of neurological disorders (NDs) is vast, and they are recognized as major causes of mortality and disability worldwide. Most existing NDs detection methods are disease-specific, which limits an algorithm's cross-disease applicability. A single diagnostic platform can save time and money over multiple diagnostic systems. There is currently no unified standard platform for diagnosing different types of NDs utilizing electroencephalogram (EEG) signal data. To address this issue, this study aims to develop a generic EEG neural Network (GENet) framework based on a convolutional neural network that can identify various NDs from EEG. The proposed framework consists of several parts: 1) preparing data using channel reduction, resampling, and segmentation for the GENet model; 2) designing and training the GENet model to carry out important features for the classification task; and 3) assessing the proposed model's performance using different signal segment lengths and several training batch sizes and also cross-validating using seven different EEG datasets of six distinct NDs namely schizophrenia, autism spectrum disorder, epilepsy, Parkinson's disease, mild cognitive impairment, and attention-deficit/hyperactivity disorder. In addition, this study also investigates whether the proposed GENet model can identify multiple NDs from EEG. The proposed model achieved much better performance for both binary and multiclass classification compared to state-of-the-art methods. In addition, the proposed model is validated using several ablation studies and layerwise feature visualization, which provide consistency and efficiency to the proposed model. The proposed GENet model will help technologists create standard software for detecting any of these NDs from EEG.