A Comparative Analysis of EEG-based Stress Detection - Utilizing Machine Learning and Deep Learning Classifiers - with a Critical Literature Review

Aug 4, 2026

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Mental stress contributes to psychological and physical diseases, making timely recognition and management essential to prevent severe outcomes such as depression, heart attack, and suicide. This survey compares EEG-based stress detection using machine learning and deep learning classifiers, with a critical literature review of the full pipeline. Pre-processing, feature extraction (PCA, ICA, DCT), and classification models—including SVM, KNN, NB, and a deep CNN—are evaluated using the DEAP dataset.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Introduction

Background

EEG Signal

Generalized process of Stress Detection

Methodology

Results

Conclusion

A Comparative Analysis ofEEG-based Stress Detection Utilizing Machine Learning and Deep Learning Classifiers with a Critical Literature Review Dipali Dhake a,b 1, Dr. Yogesh Angal a,c 2 a Department of E&TC, JSPM’s Rajarshi Shahu College of Engineering, Tathawade Pune, Maharashtra, India bDepartment of E&TC, Pimpri Chinchwad College Of Engineering And Research, Ravet Pune, Maharashtra, India [1](1 dipalippatil@gmail.com)[ dipalippatil@gmail.com](1 dipalippatil@gmail.com) cDepartment of E&TC, JSPM’s Bhivarabai Sawant Institute of Technology & Research, Wagholi Pune, Maharashtra, India [2](2 yogeshangal@yahoo.co.in)[ yogeshangal@yahoo.co.in](2 yogeshangal@yahoo.co.in) Abstract—Background: Mental stress is considered to be a major contributor to different psychological and physical diseases. Different socio-economic issues, competition in the workplace and amongst the students, and a high level of expectations are the major causes of stress. This in turn transforms into several diseases and may extend to dangerous stages if not treated properly and timely, causing the situations such as depression, heart attack, and suicide. This stress is considered to be a very serious health abnormality. Stress is to be recognized and managed before it ruins the health of a person. This has motivated the researchers to explore the techniques for stress detection. Advanced machine learning and deep learning techniques are to be investigated for stress detection. Methodology: A survey of different techniques used for stress detection is done here. Different stages of detection including pre-processing, feature extraction, and classification are explored and critically reviewed. Electroencephalogram (EEG) is the main parameter considered in this study for stress detection. After reviewing the state-of-the-art methods for stress detection, a typical methodology is implemented, where feature extraction is done by using principal component analysis (PCA), ICA, and discrete cosine transform. After the feature extraction, some state-ofart machine learning classifiers are employed for classification including support vector machine (SVM), K-nearest neighbor (KNN), NB, and CT. In addition to these classifiers, a typical deep-learning classifier is also utilized for detection purposes. The dataset used for the study is the Database for Emotion Analysis using Physiological Signals (DEAP) dataset. Results: Different performance measures are considered including precision, recall, F1-score, and accuracy. PCA with KNN, CT, SVM and NB have given accuracies of 65.7534%, 58.9041%, 61.6438%, and 57.5342% respectively. With ICA as feature extractor accuracies obtained are 58.9041%, 61.64384%, 57.5342%, and 54.79452% for the classifiers KNN, CT, SVM, and NB respectively. DCT is also considered a feature extractor with classical machine learning algorithms giving the accuracies of 56.16438%, 50.6849%, 54.7945%, and 45.2055% for the classifiers KNN, CT, SVM, and NB respectively. A conventional DCNN classification is performed given an accuracy of 76% and precision, recall, and F1-score of 0.66, 0.77, and 0.64 respectively. Conclusion: For EEG-based stress detection, different state-of-the-art machine learning and deep learning methods are used along with different feature extractors such as PCA, ICA, and DCT. Results show that the deep learning classifier gives an overall accuracy of 76%, which is a significant improvement over classical machine learning techniques with the accuracies as PCA+ KNN (65.75%), DCT+KNN (56.16%), and ICA+CT (61.64%) . Keywords-Classification, Deep Learning, Electroencephalogram, Feature extraction, Machine Learning, Stress detection. I. INTRODUCTION People experience a great deal of stress in their daily life. Stress arises from lots of medical problems like depression, heart attack, suicide, etc. As a result, it is now necessary to recognize and manage stress before it leads to serious health problems. Str

FAQs

Why is stress detection important in health and related research areas?

Stress can drive depression, heart attack, and suicide and may progress to dangerous stages if not recognized and managed early. Early detection supports timely intervention and health protection across research domains.

What EEG-based methodology pipeline is used for stress detection in this work?

The study surveys detection stages including pre-processing, feature extraction, and classification. Feature extraction is performed with PCA, ICA, and discrete cosine transform, followed by machine learning classifiers such as SVM, KNN, and NB, and a deep-learning classifier.

What dataset and models are evaluated, and what is the best reported performance?

Experiments use the DEAP dataset and evaluate performance using precision, recall, F1-score, and accuracy. A conventional DCNN classifier achieves about 76% accuracy, outperforming classical machine learning results reported for PCA-, ICA-, and DCT-based pipelines.

Aug 4, 2026

Upload by Finn