An Integrated Approach to Emotion Recognition for Advanced Emotional Intelligence

DYNAMICAL ANALYSIS OF EMOTIONAL STATES FROM ELECTROENCEPHALOGRAM SIGNALS

The current study evaluates the dynamics of the electroencephalogram signals during specific emotional states in order to obtain a detailed understanding of the affective EEG patterns. Employing recurrence analysis, the dynamical states of the emotional brain during visual stimuli is evaluated. Three channels of electroencephalogram time series (Fz, Cz, and Pz) available in eNTERFACE06_EMOBRAIN database are used in this study. Electroencephalogram signals are recorded from 5 subjects in three emotional categories: exciting negative (disgust), neutral and exciting positive (happy). Recurrence quantification analysis (RQA) was applied to study electroencephalogram morphological changes in different emotional states (happy, disgust, neutral). The ANOVA and [Formula: see text]-test are done to detect significant differences in RQA measures of the EEGs. It has shown that the phase space trajectory becomes more periodic during exciting negative. In addition, the results reveal that in comparison with negative emotion and neutral, the behavior of EEGs in positive emotion is highly chaotic. Performing statistical analysis, significant differences were observed in recurrence rate, determinism, average diagonal, line length, Shannon entropy, laminarity, trapping time among three emotional evoked groups. Although these changes occurred in all EEG channels, a better distinction between each emotional state can be observed in Pz location. It seems that recurrence analysis is a promising non-linear approach for detecting instantaneous changes in the emotional EEG induced by visual stimuli. RQA has the potential to discover differences of signal features in response to an emotional stimulus.

Single-trial EEG-based emotion recognition using kernel Eigen-emotion pattern and adaptive support vector machine

Single-trial electroencephalography (EEG)-based emotion recognition enables us to perform fast and direct assessments of human emotional states. However, previous works suggest that a great improvement on the classification accuracy of valence and arousal levels is still needed. To address this, we propose a novel emotional EEG feature extraction method: kernel Eigen-emotion pattern (KEEP). An adaptive SVM is also proposed to deal with the problem of learning from imbalanced emotional EEG data sets. In this study, a set of pictures from IAPS are used for emotion induction. Results based on seven participants show that KEEP gives much better classification results than the widely-used EEG frequency band power features. Also, the adaptive SVM greatly improves classification performance of commonly-adopted SVM classifier. Combined use of KEEP and adaptive SVM can achieve high average valence and arousal classification rates of 73.42% and 73.57%. The highest classification rates for valence and arousal are 80% and 79%, respectively. The results are very promising.

Corporate psychopathy: deviant workplace behaviour and toxic leaders (part two)

Purpose

– Increasingly, it is recognized that (larger) organizations have many employees who present with corporate psychopathy (i.e. a milder version of anti-social personality disorder). Importantly, such a disorder contributes to the presence of deviant workplace behavior. Organizations must therefore adapt its practices to both identify and manage employees who either present with, or have tendencies toward, corporate psychopathy. As a means of developing a guiding framework for organizational adaptation, this two part paper offers two reviews of relevant research. The first revolves around the body of knowledge regarding corporate psychopathy and the primary, established behavioral method of identifying its presence; the second is a brief review on physiological measures that can complement current gold standards. The paper aims to discuss these issues.

Design/methodology/approach

– A range of published empirical and practitioner research articles were reviewed to first, elaborate on anti-social personality disorder and corporate psychopathy; second, showcase the efficacy of the currently most accepted method of detecting psychopathic behavior – the Psychopathy Checklist (PCL); and third, highlight physiological methods of detecting psychopathic tendencies which may complement usage of the PCL – electroencephalography, measurement of galvanic skin responses, and electromyography.

Findings

– First, deviant workplace behaviors cause losses of billions of dollars across all business organizations, and much of this behavior stems from corporate psychopaths in positions of leadership. Second, the PCL, while useful, can nonetheless yield sharp differences in the identification of psychopathy across different administrators of the test. Third, measures of physiological states show good reliability in discriminating psychopathic persons from non-psychopathic persons. Based on these findings, the authors propose guidelines for how to identify and mitigate the effects of corporate psychopathy for organizations.

Research limitations/implications

– The proposed guidelines must be tested in an empirical paper to measure their effectiveness.

Practical implications

– The paper suggests an overall framework that may help leaders and organizational development practitioners identify the major factors which may be considered to safeguard against the potentially detrimental conduct of corporate psychopaths in their organizations.

Social implications

– This paper highlights the need to identify and ward against the presence of corporate psychopaths. There needs to be guidelines for organizations on how to identify and mitigate the effects of corporate psychopathy for organizations.

Originality/value

– The suggestion of integrating physiological methods of detection with the PCL, as well as urging pro-active education of all employees as the symptoms and effects of corporate psychopathy, is the novel contribution of the paper.

Corporate psychopathy: deviant workplace behaviour and toxic leaders – part one

Purpose

– Increasingly, it is recognized that (larger) organizations have many employees who present with corporate psychopathy (i.e. a milder version of antisocial personality disorder (APD)). Importantly, such a disorder contributes to the presence of deviant workplace behaviour. Organizations must therefore adapt its practices to both identify and manage employees who either present with, or have tendencies towards, corporate psychopathy. As a means of developing a guiding framework for organizational adaptation, the purpose of this two-part paper is to offer two reviews of relevant research. The first revolves around the body of knowledge regarding corporate psychopathy and the primary, established behavioural method of identifying its presence; the second is a brief review on physiological measures that can complement current gold standards.

Design/methodology/approach

– A range of published empirical and practitioner research articles were reviewed to elaborate on APD and corporate psychopathy; showcase the efficacy of the currently most accepted method of detecting psychopathic behaviour – the Psychopathy Checklist (PCL) and; highlight physiological methods of detecting psychopathic tendencies which may complement usage of the PCL – electroencephalography, measurement of galvanic skin responses, and electromyography.

Findings

– Deviant workplace behaviours cause losses of billions of dollars across all business organizations, and much of this behaviour stems from corporate psychopaths in positions of leadership; the PCL, while useful, can nonetheless yield sharp differences in the identification of psychopathy across different administrators of the test; measures of physiological states show good reliability in discriminating psychopathic persons from non-psychopathic persons. Based on these findings, the authors propose guidelines for how to identify and mitigate the effects of corporate psychopathy for organizations.

Research limitations/implications

– The proposed guidelines must be tested in an empirical paper to measure their effectiveness.

Practical implications

– The paper suggests an overall framework that may help leaders and organizational development practitioners identify the major factors which may be considered to safeguard against the potentially detrimental conduct of corporate psychopaths in their organizations.

Social implications

– This paper highlights the need to identify and ward against the presence of corporate psychopaths. There needs to be guidelines for organizations on how to identify and mitigate the effects of corporate psychopathy for organizations.

Originality/value

– The suggestion of integrating physiological methods of detection with the PCL, as well as urging proactive education of all employees as the symptoms and effects of corporate psychopathy, is the novel contribution of the paper.

Real time emotion aware applications: a case study employing emotion evocative pictures and neuro-physiological sensing enhanced by Graphic Processor Units

In this paper the feasibility of adopting Graphic Processor Units towards real-time emotion aware computing is investigated for boosting the time consuming computations employed in such applications. The proposed methodology was employed in analysis of encephalographic and electrodermal data gathered when participants passively viewed emotional evocative stimuli. The GPU effectiveness when processing electroencephalographic and electrodermal recordings is demonstrated by comparing the execution time of chaos/complexity analysis through nonlinear dynamics (multi-channel correlation dimension/D2) and signal processing algorithms (computation of skin conductance level/SCL) into various popular programming environments. Apart from the beneficial role of parallel programming, the adoption of special design techniques regarding memory management may further enhance the time minimization which approximates a factor of 30 in comparison with ANSI C language (single-core sequential execution). Therefore, the use of GPU parallel capabilities offers a reliable and robust solution for real-time sensing the user's affective state.

On the classification of emotional biosignals evoked while viewing affective pictures: an integrated data-mining-based approach for healthcare applications

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.