Differentiating Mental Stress Levels - Analysing Machine Learning Algorithms Comparatively For EEG-Based Mental Stress Classification Using MNE-Python

Aug 5, 2026

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Mental stress affects well-being and productivity, and EEG-based classification enables early detection and timely intervention. This study evaluates how different machine learning models perform when EEG signals are processed with MNE-Python, using a single dataset to ensure consistent comparison. After MNE-Python pre-processing (signal cleaning and feature selection), models including RF, Decision Tree, KNN, MLP, SVM, Adaboost, and XGBoost are trained and assessed via accuracy, precision, recall, and F1-score. Results show clear algorithm-dependent differences, highlighting the importance of proper model selection for reliable mental stress monitoring systems for healthcare, education, and workplace settings.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

EEG and Stress Assessment

Journal of Advanced Zoology ISSN: 0253-7214 Volume 44 IssueS-5 Year 2023 Page 2605:2618 Differentiating Mental Stress Levels: Analysing Machine Learning Algorithms Comparatively For EEG-Based Mental Stress Classification Using MNE-Python Soumya Samarpita1*, Rabinarayan Satpathy2, Bibhu Kalyan Mishra3, Rudra Prasanna Mishra4 1,2,3Faculty of Science, Sri Sri University, Cuttack, Odisha 4Faculty of Emerging Technology, Sri Sri University, Cuttack, Odisha [Email:](Email:1s.samarpita89@gmail.com)[1](Email:1s.samarpita89@gmail.com)[s.samarpita89@gmail.com](Email:1s.samarpita89@gmail.com), [2](2rabinarayan.s@srisriuniversity.edu.in)[rabinarayan.s@srisriuniversity.edu.in](2rabinarayan.s@srisriuniversity.edu.in), [3](3bibhu.m@srisriuniversity.edu.in)[bibhu.m@srisriuniversity.edu.in](3bibhu.m@srisriuniversity.edu.in), [4](4rudra.m2022btcseai@srisriuniversity.edu.in)[rudra.m2022btcseai@srisriuniversity.edu.in](4rudra.m2022btcseai@srisriuniversity.edu.in) Corresponding Author Email: [s.samarpita89@gmail.com](s.samarpita89@gmail.com) | Article History
Received: 21 June 2023
Revised: 12 Sept 2023
Accepted: 22 Nov 2023
CC License
CC-BY-NC-SA 4.0 | Abstract
Mental stress is a prevalent and consequential condition that impacts individuals' well-being and productivity. Accurate classification of mental stress levels using electroencephalogram (EEG) signals is a promising avenue for early detection and intervention. In this study, we present a comprehensive investigation into mental stress classification using EEG data processed with the MNE-Python library. Our research leverages a diverse set of machines learning algorithms, including Random Forest (RF), Decision Tree, K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Adaboost, and Extreme Gradient Boosting (XGBoost), to discern differences in classification performance. We employed a single dataset to ensure consistency in our experiments, facilitating a direct comparison of these algorithms. The EEG data were pre-processed using MNE-Python, which included tasks such as signal cleaning, and feature selection. Subsequently, we applied the selected machine learning models to the processed data and assessed their classification performance in terms of accuracy, precision, recall, and F1-score. Our results demonstrate notable variations in the classification accuracy of mental stress levels across the different algorithms. These findings suggest that the choice of machine learning technique plays a pivotal role in the effectiveness of EEG-based mental stress classification. Our study not only highlights the potential of MNE-Python for EEG signal processing but also provides valuable insights into the selection of appropriate machine learning algorithms for accurate and reliable mental stress assessment. These outcomes hold promise for the development of robust and practical systems for real-time mental stress monitoring, contributing to enhanced well-being and performance in various domains such as healthcare, education, and workplace environments.
Keywords: Mental Stress, EEG Signals, MNE-Python, Classification, Machine Learning Algorithms. | | --- | --- | 1. Introduction Mental stress, usually referred to as psychological or emotional stress, is a significant problem in contemporary culture. It appears when people believe they can't successfully handle the expectations being placed on them. This view may be caused by a variety of things, including demands at work, academic obstacles, financial difficulties, interpersonal disputes, and life events. If stress is not well controlled, it can seriously harm both a person's physical and mental health. It is associated with a range of health issues, including cardiovascular problems, compromised immune function, anxiety disorders, depression, and reduced cognitive performance. Additionally, chronic stress can contribute to the development of more severe mental health c

FAQs

How are model performances evaluated?

Model outputs are evaluated using accuracy, precision, recall, and F1-score, after EEG preprocessing in MNE-Python including signal cleaning and feature selection.

Aug 5, 2026

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