Analysis of Multimodal Physiological Signals Within and Across Individuals to Predict Psychological Threat vs. Challenge

Abstract

Challenge and threat are biopsychological responses following an individual's evaluation of task demands relative to his or her available resources to cope with these demands. In this study, we aimed to investigate individual and group variation in physiological responding across a series of motivated performance tasks of varying difficulty. We specifically tested three hypotheses: (H1) individuals will express different sets of physiological patterns (features) across tasks of varying difficulty; (H2) there willbe groups of individuals who share common salient physiological features that dominate within-individual differentiation in physiological responding across tasks of varying difficulty; and (H3) the accuracy of predicting self-reported judgments of challenge and threat across individuals will be higher within each group with shared salient physiological features than across all groups or the entire sample. To test these hypotheses, we developed an integrated analytic framework for multimodal physiological data analysis. We employed data from an existing experiment in which participants completed three mental arithmetic tasks of increasing difficulty during which we collected different modalities of physiological data. Analyses revealed three groups of participants who shared common features that best differentiated their within-individual physiological response patterns across tasks. Support vector machine (SVM) classifiers were then trained using both shared features within each group and all computed features to predict challenge vs. threat states. Our results showed that, within-group classification model using person-specific features achieved higher self-report prediction accuracy comparing to the alternative model trained on data from all participants without feature selection.

An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data

“…Data from 107 participants (65 females) were randomly selected from a larger dataset of 260 participants. All of the physiological measures included in our Bio-SP tool were recorded from each participant while he/she was performing a series of motivated performance tasks [7] . All biosignals were recorded using BioLab v.3.0.13 (Mindware Technologies; Gahanna, OH), and were acquired on a BioNex 8-Slot Chassis with a sampling rate of 1000 Hz.…”

“…6 . To quantitatively evaluate the performance of this algorithm, we compared B points detected by this algorithm to B points detected by visual inspection and manual marking by trained scorers for 150 randomly selected cardiac cycles from 20 different participants [7] . Results shown in Fig.…”

“…Measures of peripheral physiological signals (biosignals), including the electrocardiogram (ECG), impedance cardio- gram (ICG), electromyogram (EMG), electrodermal activity (EDA) and continuous, noninvasive arterial blood pressure (BP) can provide rich information about peripheral physiological functioning of the body with the potential for providing insights on phenomena such as health status, sleep quality, and the affective properties of psychological experience. These measures are thus widely used as the input data for machine learning and pattern recognition algorithms focused on preventive care, diagnostics, and telemedicine, including guiding therapy [1] – [8] . For example, a support vector machine (SVM) classifier was trained on ECG signal features, such as inter-beat interval (IBI) to predict ischaemic heart disease in [2] , and an SVM was trained on EDA signal features to corroborate coding by adult experimenters of groups of children who were either easy or hard to engage socially during a set of lab interaction tasks [3] .…”

“…In [4] , an inference model trained on EMG signal features was used to aid the diagnosis of neuromuscular disorders, and in [5] , a model trained on BP signal features was used to aid in diagnosing diabetes. Valvular heart diseases have been predicted by models using ICG signal features [6] and physiological responding under stress has been studied by analyzing multimodal data comprising ECG, EMG, EDA, and ICG signal features [7] . These are just a few examples of the rapidly expanding research utilizing the information present in biosignals for machine learning and pattern recognition in healthcare and psychological science [7] , [9] .…”

“…The present work describes an open-source Biosignal-Specific Processing toolbox (Bio-SP tool) for psychologists, neuroscientists, and researchers in affective computing, machine learning and pattern classification that can be used to process ECG, EMG, EDA, ICG and continuous BP biosignals all in one easy-to-use, MATLAB-based toolbox. Bio-SP tool extracts relevant features for each signal automatically and reliably, and has been used in affective computing studies for processing and feature extraction with peripheral physiological biosignals [7] , [25] . In this paper, we provide additional background information about the Bio-SP tool.…”