Human stress classification during public speaking using physiological signals

• Published in: Computers in biology and medicine Vol. 133; p. 104377
• Main Authors: Arsalan, Aamir, Majid, Muhammad
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.06.2021; Elsevier Limited
• Subjects: Accelerometers; Accuracy; Anxiety; Classification; Classifiers; Conductance; EEG; Electrocardiography; Electroencephalography; Feature extraction; Frequencies; Galvanic skin response; Heart rate; Human body; Human stress wearable sensors physiological signals multimodal fusionPublic speaking; Machine learning; Physiology; Psychological stress; Public speaking; Quantitative psychology; Questionnaires; Radial basis function; Resistance; Sensors; Skin; Statistical analysis; Statistics; Stress; Support vector machines; Human stress wearable sensors physiological signals multimodal fusionPublic speaking
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2021.104377

Public speaking is a common type of social evaluative situation and a significant amount of the population feel uneasy with it. It is of utmost importance to detect public speaking stress so that appropriate action can be taken to minimize its impacts on human health. In this study, a multimodal human stress classification scheme in response to real-life public speaking activity is proposed. Electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG) signals of forty participants are acquired in rest-state and during public speaking activity to divide data into a stressed and non-stressed group. Frequency domain features from EEG and time-domain features from GSR and PPG signals are extracted. The selected set of features from all modalities are fused to classify the stress into two classes. Classification is performed via a leave-one-out cross-validation scheme by using five different classifiers. The highest accuracy of 96.25% is achieved using a support vector machine classifier with radial basis function. Statistical analysis is performed to examine the significance of EEG, GSR, and PPG signals between the two phases of the experiment. Statistical significance is found in certain EEG frequency bands as well as GSR and PPG data recorded before and after public speaking supporting the fact that brain activity, skin conductance, and blood volumetric flow are credible measures of human stress during public speaking activity. •A human stress classification framework in response to public speaking is proposed.•EEG, GSR, and PPG signals are acquired during rest-state and public speaking.•Beta band of EEG is identified as significant for acute stress classification.•Fusion of selected features from EEG, GSR, and PPG signals gives highest accuracy.