Research Article
EEG-Based Stress Detection Using K-Means Clustering Method
@INPROCEEDINGS{10.1007/978-3-031-35078-8_4, author={Soumya Samarpita and Rabinarayan Satpathy}, title={EEG-Based Stress Detection Using K-Means Clustering Method}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part I}, proceedings_a={ICISML}, year={2023}, month={7}, keywords={Brain Waves Mental Stress Electro-encephalogram (EEG) EEG Signals K-Means clustering}, doi={10.1007/978-3-031-35078-8_4} }- Soumya Samarpita
Rabinarayan Satpathy
Year: 2023
EEG-Based Stress Detection Using K-Means Clustering Method
ICISML
Springer
DOI: 10.1007/978-3-031-35078-8_4
Abstract
Stress, sadness and panic have all become major issues in our contemporary culture. Stress has become one of the top ten socioeconomic predictors of health inequalities. The electroencephalogram (EEG) signals and machine learning approaches are utilized to predict the mental state of the person. This has become a significant topic of research in recent times in health care system. There are various ways are used to monitor stress. The primary goal of this study is to identify stress in humans. Because of its potential value, stress detection based on EEG signals has emerged as an interesting study topic. This research looks into brain waves to classify a person’s mental state. Despite the fact that there is no precise way of defining the optimum feature for a classifier, the features utilized as classifier input have a significant impact on the classification outcomes. An algorithm for stress level detection from EEG is proposed in this paper. The Euclidean distance scale is commonly used in the paper for EEG signal identification. In this study, EEG data is separated into EEG rhythms using a band pass filter method, EEG signals are normalized and a k-mean clustering method is used to classify brain wave signals to detect the mental stress.
