Abstract
In the present study, we applied Machine Learning (ML) methods to identify psychobiological markers of cognitive processes involved in the process of emotion elicitation as postulated by the Component Process Model (CPM). In particular, we focused on the automatic detection of five appraisal checks—novelty, intrinsic pleasantness, goal conduciveness, control, and power—in electroencephalography (EEG) and facial electromyography (EMG) signals. We also evaluated the effects on classification accuracy of averaging the raw physiological signals over different numbers of trials, and whether the use of minimal sets of EEG channels localized over specific scalp regions of interest are sufficient to discriminate between appraisal checks. We demonstrated the effectiveness of our approach on two data sets obtained from previous studies. Our results show that novelty and power appraisal checks can be consistently detected in EEG signals above chance level (binary tasks). For novelty, the best classification performance in terms of accuracy was achieved using features extracted from the whole scalp, and by averaging across 20 individual trials in the same experimental condition (UAR = 83.5 ± 4.2; N = 25). For power, the best performance was obtained by using the signals from four pre-selected EEG channels averaged across all trials available for each participant (UAR = 70.6 ± 5.3; N = 24). Together, our results indicate that accurate classification can be achieved with a relatively small number of trials and channels, but that averaging across a larger number of individual trials is beneficial for the classification for both appraisal checks. We were not able to detect any evidence of the appraisal checks under study in the EMG data. The proposed methodology is a promising tool for the study of the psychophysiological mechanisms underlying emotional episodes, and their application to the development of computerized tools (e.g., Brain-Computer Interface) for the study of cognitive processes involved in emotions.
Highlights
Research in the affective sciences aims at understanding the mechanisms driving human emotion
We describe an application of Machine Learning (ML) to the detection of EEG and facial EMG signal patterns related to the processing of appraisal checks
We focused on determining whether various stages of event evaluation as postulated by appraisal theories can be automatically detected in this type of psychophysiological signals
Summary
Research in the affective sciences aims at understanding the mechanisms driving human emotion (and related processes). A definition of emotion that all emotion researchers would agree on is lacking (see e.g., [1,2,3]), emotions can generally be described as responses to events that are important to an individual, and typically include cognitions, action tendencies, bodily responses, expression and subjective feelings (see e.g., [1, 3, 4]). The unique combination of the outcomes for the different appraisal criteria determines the type and intensity of the elicited emotion(s) This outcome will in turn orchestrate a series of (coordinated) responses in the so-called emotion components such as motivation (e.g., approach or avoidance), bodily responses (e.g., cardiovascular changes), expression (facial, vocal, and gesture), and subjective feelings (the conscious experience of an emotion) (see [5] for an overview). In the current work, we focus on appraisal models, as these make the most specific predictions about these cognitive mechanisms
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