Analysing cognitive states from EEG signals with deep learning techniques

Integrating deep learning and advanced preprocessing techniques in Electroencephalography (EEG) data is used in this paper to present a new way to improve the classification of mental illnesses. That’s why these structures are so important for figuring out mental illnesses, especially when the EEGNet and TSCeption models are used, which sort cognitive processes in brain waves into groups. By changing the time-frequency and feature extractors, the model becomes much better at classifying cognitive states, which leads to higher sensitivity, accuracy, and lower error rates. This method increases the usefulness of deep learning neural networks in areas like neurofeedback and brain-computer interfaces because they can quickly and accurately check cognitive signs and find brain disorders early on. This is the first proof that it is possible to combine deep learning with EEG signals to create a useful framework for cognitive classification. The study aims to advance future innovations in the detection and treatment of automated delusions and bipolar disorders with EEG and deep learning technology.