Automatic classification of EEG signals via deep learning

Electroencephalogram (EEG) is widely used to diagnose many neurological and psychiatric brain disorders. The correct interpretation of EEG data is critical to avoid misdiagnosis. However, the analysis of EEG data requires trained specialists and may vary from expert to expert. Meanwhile, it can be challenging and time-consuming to assess the EEG data since these signals may last several hours or days. Therefore, rapid and accurate classification of EEG data may be a key step towards interpreting EEG records. In this study, a novel deep learning model with an end-to-end structure is proposed to distinguish normal and abnormal EEG signals automatically. For this purpose, we investigate the possibility of combining the core ideas of inception and residual architectures into a hybrid model to improve classification performance. We evaluated the proposed method through extensive experiments on a real-world dataset, and it shows feasibility and effectiveness. Compared to previous studies on the same data, our method outperforms other existing EEG signal methods. Thus, the proposed method can aid clinicians to automatically detect brain activity.