# Classification of Perceived Human Stress using Physiological Signals

## TL;DR

This study demonstrates that classifying perceived human stress using physiological signals like EEG, GSR, and PPG can achieve up to 75% accuracy, outperforming existing methods without stress inducers.

## Contribution

The paper introduces a novel approach combining EEG, GSR, and PPG signals with multiple classifiers to improve perceived stress classification accuracy.

## Key findings

- MLP classifier achieved 75% accuracy.
- Physiological signals can effectively classify perceived stress.
- Proposed method outperforms existing non-stress-inducing classification techniques.

## Abstract

In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We conducted experiments consisting of steps including data acquisition, feature extraction, and perceived human stress classification. The physiological data of $28$ participants are acquired in an open eye condition for a duration of three minutes. Four different features are extracted in time domain from EEG, GSR and PPG signals and classification is performed using multiple classifiers including support vector machine, the Naive Bayes, and multi-layer perceptron (MLP). The best classification accuracy of 75% is achieved by using MLP classifier. Our experimental results have shown that our proposed scheme outperforms existing perceived stress classification methods, where no stress inducers are used.