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

International Journal of Electronics and Communication Engineering

© 2023 by SSRG - IJECE Journal

Volume 10 Issue 5

Year of Publication : 2023

Authors : Shailaja Kotte, J R K Kumar Dabbakuti

10.14445/23488549/IJECE-V10I5P108

How to Cite?

Shailaja Kotte, J R K Kumar Dabbakuti, "EEG Signal in Emotion Detection Feature Extraction and Classification using Fuzzy Based Feature Search Algorithm and Deep Q Neural Network in Deep Learning Architectures," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 5, pp. 85-95, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I5P108

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.

Keywords:

Electroencephalography, Emotion detection, Deep learning, Feature extraction, Classification, Neural networks.

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