EEG Signal Classification for Mental States Using Deep Learning

Abdulrahman W. H. Al-Askari^1,*

¹Northern Technical University, Iraq

Email:  abdulrahman21@ntu.edu.iq

Abstract

In recent years, EEG based recognition and characterization of brain states has received much interest due to the advances in deep learning and machine learning methods. The non-invasive and highly inexpensive activity of EEG presents a patient with details concerning the activity and the conditions of the brain. The synthesis of artificial intelligence (AI) models (convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and collaborative knowledge options has been explored in a series of studies that recognize the mental state accurately in a large number of cases. The literature focuses on introducing strong, explainable models as well as on multimodal data to boost classification accurateness and reliability. The results are a 1D CNN and a LSTM network were trained separately and in a hybrid, architecture (CNN-LSTM) to classify the EEG signals. The models were appraised using accurateness, accuracy, recollection, F1-score, and confusion matrix analysis.

Keywords: EEG; Deep Learning; Mental State Classification; CNN-LSTM; Brain-Computer Interface