A Parallel Fusion RNN-LSTM Approach to Classify Mental Stress using EEG Data

A Parallel Fusion RNN-LSTM Approach to Classify Mental Stress using EEG Data

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© 2022 by IJETT Journal
Volume-70 Issue-10
Year of Publication : 2022
Authors : Megha V. Gupta, Shubhangi L. Vaikole
DOI : 10.14445/22315381/IJETT-V70I10P228

How to Cite?

Megha V. Gupta, Shubhangi L. Vaikole, "A Parallel Fusion RNN-LSTM Approach to Classify Mental Stress using EEG Data," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 285-297, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P228

Abstract
Preventing and physiological healing problems need early stress diagnosis and a participant's predisposition to operate healthily under stress. Traditional methods of evaluating anxiety levels, such as interviewing the person and having to ask strain-based queries to develop a better understanding of their situation and observing facial gestures - individuals under stress respond by changing their brows, pupils dilating, or one’s flashing strobe percentage could differentiate - are limited even though they may overlook stress episodes. Electroencephalogram (EEG) is a newly created physiological measure that has the potential to be utilized as a stress gauge in everyday life. It is due to the commercial availability of EEG headsets for studying brain activity conveniently and cost-effectively. This investigation used machine learning methods to classify stress status using resting-state EEG signal recordings. The method was tested using a dataset from the MathWorks® EEGLAB toolbox, and a dataset of 20 patients was constructed using a questionnaire and Neurosky's Mindwave EEG headset. For stress detection, a support vector machine (SVM), recurrent neural network (RNN), long short-term memory (LSTM), and a novel technique based on a parallel fusion of RNN-LSTM are used. The results of the MATLAB simulations show that the proposed technique is faster and more accurate than other machine-learning approaches. The proposed technique has a 95% accuracy rate, up to a 15% improvement over other results.

Keywords
Stress detection, EEG signals, machine learning, EEGLAB toolbox, Meurosky's Mindwave EEG headset, MATLAB.

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