On the Classification of Emotional Biosignals Evoked While Viewing Affective Pictures: An Integrated Data-Mining-Based Approach for Healthcare Applications

Abstract

Recent neuroscience findings demonstrate the fundamental role of emotion in the maintenance of physical and mental health. In the present study, a novel architecture is proposed for the robust discrimination of emotional physiological signals evoked upon viewing pictures selected from the International Affective Picture System (IAPS). Biosignals are multichannel recordings from both the central and the autonomic nervous systems. Following the bidirectional emotion theory model, IAPS pictures are rated along two dimensions, namely, their valence and arousal. Following this model, biosignals in this paper are initially differentiated according to their valence dimension by means of a data mining approach, which is the C4.5 decision tree algorithm. Then, the valence and the gender information serve as an input to a Mahalanobis distance classifier, which dissects the data into high and low arousing. Results are described in Extensible Markup Language (XML) format, thereby accounting for platform independency, easy interconnectivity, and information exchange. The average recognition (success) rate was 77.68% for the discrimination of four emotional states, differing both in their arousal and valence dimension. It is, therefore, envisaged that the proposed approach holds promise for the efficient discrimination of negative and positive emotions, and it is hereby discussed how future developments may be steered to serve for affective healthcare applications, such as the monitoring of the elderly or chronically ill people.

Decision theoretic modeling of affective and cognitive needs for product experience engineering: key issues and a conceptual framework

“…Despite capability of interpreting self-report data, the subjective methods suffer from recall and selective reporting biases (Stone and Shiffman 1994). Recent efforts have been geared towards an objective route (neurophysiology-oriented) by means of monitoring the participant's behavioral indicators or physiological signals (Frantzidis et al 2010). The main advantage of objective methods is the avoidance of interference to the participant and possibility of real-time data collection.…”

Brain Data Analysis and Management

“…The goal of this study is to distinguish emotional bio-signals evoked by viewing selected affective pictures from the International Affective Picture System (IAPS) [5]. EEG-based brain-computer interfaces consist of very typical components, each of which performs its own critical function.…”

Electroencephalogram-based emotion assessment system using ontology and data mining techniques

“…and (2) How can we measure and predict user emotional states in the interaction process? These two questions are often approached in the form of either a subjective route (psychology-oriented) by means of monitoring/observing questionnaire responses of a subject, or a more objective route (neurophysiologyoriented) by means of monitoring the subject's behavioral indicators and/or physiological signals [3,4]. Subjective approaches generally have some weaknesses, such as selective reporting biases by users, interference with data collection in real time, and further resistance to data analysis required in a vast number of HCI applications [3].…”