EEG Signal in Emotion Detection Feature Extraction and Classification using Fuzzy Based Feature Search Algorithm and Deep Q Neural Network in Deep Learning Architectures

Abstract

EEG is a non-invasive method of recording evoked and induced electrical activity in the brain from the scalp. EEG data is increasingly used in artificial intelligence (A.I.) applications, including pattern recognition, group membership categorization, and brain-computer interface resolutions. This study presents unique EEG data approaches for emotion detection, feature extraction, and classification utilizing fuzzy-based deep learning techniques. This step has analyzed and separated The incoming EEG data as signal fragments. This signal has been pre-processed to remove and normalize noise for feature extraction. The processed signal was retrieved using a fuzzy neural network (FNN) for features. A deep Q neural network was used to classify these retrieved features. Four performance indicators, namely accuracy of 96%, Precision of 90%, Sensitivity of 92%, Specificity of 90% RMSE of 88% for 500 epochs, were used to assess the performance of four distinct classifiers. This investigation indicated that the proposed feature extraction method could accurately identify EEG data recorded during a demanding task. As a result, the suggested feature selection and optimization approach can potentially enhance classification accuracy.