  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.
