Towards a constructionist approach to emotions: verification of the three-dimensional model of affect with EEG-independent component analysis

The affective grounds of the mind. The Affective Pertinentization (APER) model

The paper presents the Affective Pertinentization model (APER), a theory of the affect and its role it plays in meaning-making. APER views the affect as the basic form of making sense of reality. It consists of a global, bipolar pattern of neurophysiological activity through which the organism maps the instant-by-instant variation of its environment. Such a pattern of neuropsychological activity is constituted by a plurality of bipolar affective dimensions, each of which maps a component of the environmental variability. The affect has a pluri-componential structure defining a multidimensional affective landscape that foregrounds (i.e., makes pertinent) a certain pattern of facets of the environment (e.g., its pleasantness/unpleasantness) relevant to survival, while backgrounding the others. Doing so, the affect grounds the following cognitive processes. Accordingly, meaning-making can be modeled as a function of the dimensionality of the affective landscape. The greater the dimensionality of the affective landscape, the more differentiated the system of meaning is. Following a brief review of current theories pertaining to the affect, the paper proceeds discussing the APER's core tenets - the multidimensional view of the affect, its semiotic function, and the concepts of Affective Landscape and Phase Space of Meaning. The paper then proceeds deepening the relationship between the APER model and other theories, highlighting how the APER succeeds in framing original conceptualizations of several challenging issues - the intertwinement between affect and sensory modalities, the manner in which the mind constitutes the content of the experience, the determinants of psychopathology, the intertwinement of mind and culture, and the spreading of affective forms of thinking and behaving in society. Finally, the unsolved issues and future developments of the model are briefly envisaged.

Addressing the elephant in the room: understanding functional neurological symptom disorder through the lens of culture and religion

Owing to the dearth of scholarly works to understand the presence of Functional Neurological Symptom Disorder (FNSD) among mental health patients in Pakistan, this study sought to understand how cultural and religious conflicts are implicated in the aetiology of FNSD. The study recruited 22 participants, comprising five men and 17 women. The participants were recruited from the Department of Psychiatry at Services Hospital, Lahore, Pakistan. Semi-structured interviews were conducted and analyzed through Thematic Analysis. The two main themes identified in this study were cultural and religious values and beliefs about romantic relationships. Within the cultural and religious values theme, subthemes of self-perception, a conviction in religious beliefs, and sexual suppression were identified. Furthermore, the subthemes of beliefs about romantic relationships were family's approval, engagement against wishes, and fear of exposure. The two main themes are interconnected: beliefs about romantic relationships were interpreted and experienced through the perspective of religion and culture. To summarize, this study concluded that stressors related to culture and religion are significant contributing factors in the development of FNSD. This study has important implications for mental health professionals, as awareness around the interplay of cultural as well as religious beliefs and FNSD will enable them to devise effective and holistic therapeutic intervention.

Electroencephalography based emotion detection using ensemble classification and asymmetric brain activity

Over the past decade, emotion detection using rhythmic brain activity has become a critical area of research. The asymmetrical brain activity has garnered the most significant level of research attention due to its implications for the study of emotions, including hemispheric asymmetry or, more generally, asymmetrical brain activity. This study aimed at enhancing the accuracy of emotion detection using Electroencephalography (EEG) brain signals. This happens by identifying electrodes where relevant brain activity changes occur during the emotions and by defining pairs of relevant electrodes having asymmetric brain activities during emotions. Experimental results showed that the proposed method is highly competitive compared with existing studies of multi-class emotion recognition. These results were improved by processing not the whole EEG signals but by focusing on fragments of the signals, called epochs, which represent the instants where the excitation is maximum during emotions. The epochs were extracted using the zero-time windowing method and the numerator group-delay function.

EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels

Emotion recognition is receiving significant attention in research on health care and Human-Computer Interaction (HCI). Due to the high correlation with emotion and the capability to affect deceptive external expressions such as voices and faces, Electroencephalogram (EEG) based emotion recognition methods have been globally accepted and widely applied. Recently, great improvements have been made in the development of machine learning for EEG-based emotion detection. However, there are still some major disadvantages in previous studies. Firstly, traditional machine learning methods require extracting features manually which is time-consuming and rely heavily on human experts. Secondly, to improve the model accuracies, many researchers used user-dependent models that lack generalization and universality. Moreover, there is still room for improvement in the recognition accuracies in most studies. Therefore, to overcome these shortcomings, an EEG-based novel deep neural network is proposed for emotion classification in this article. The proposed 2D CNN uses two convolutional kernels of different sizes to extract emotion-related features along both the time direction and the spatial direction. To verify the feasibility of the proposed model, the pubic emotion dataset DEAP is used in experiments. The results show accuracies of up to 99.99% and 99.98 for arousal and valence binary classification, respectively, which are encouraging for research and applications in the emotion recognition field.

The dynamics of pain reappraisal: the joint contribution of cognitive change and mental load

This study was designed to investigate the neural mechanism of cognitive modulation of pain via a reappraisal strategy with high temporal resolution. The EEG signal was recorded from 29 participants who were instructed to down-regulate, up-regulate, or maintain their pain experience. The L2 minimum norm source reconstruction method was used to localize areas in which a significant effect of the instruction was present. Down-regulating pain by reappraisal exerted a robust effect on pain processing from as early as ~100 ms that diminished the activity of limbic brain regions: the anterior cingulate cortex, right orbitofrontal cortex, left anterior temporal region, and left insula. However, compared with the no-regulation condition, the neural activity was similarly attenuated in the up- and down-regulation conditions. We suggest that this effect could be ascribed to the cognitive load that was associated with the execution of a cognitively demanding reappraisal task that could have produced a general attenuation of pain-related areas regardless of the aim of the reappraisal task (i.e., up- or down-regulation attempts). These findings indicate that reappraisal effects reflect the joint influence of both reappraisal-specific (cognitive change) and unspecific (cognitive demand) factors, thus pointing to the importance of cautiously selected control conditions that allow the modulating impact of both processes to be distinguished.

Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification

Recognizing emotions using biological brain signals requires accurate and efficient signal processing and feature extraction methods. Existing methods use several techniques to extract useful features from a fixed number of electroencephalography (EEG) channels. The primary objective of this study was to improve the performance of emotion recognition using brain signals by applying a novel and adaptive channel selection method that acknowledges that brain activity has a unique behavior that differs from one person to another and one emotional state to another. Moreover, we propose identifying epochs, which are the instants at which excitation is maximum, during the emotion to improve the system’s accuracy. We used the zero-time windowing method to extract instantaneous spectral information using the numerator group-delay function to accurately detect the epochs in each emotional state. Different classification scheme were defined using QDC and RNN and evaluated using the DEAP database. The experimental results showed that the proposed method is highly competitive compared with existing studies of multi-class emotion recognition. The average accuracy rate exceeded 89%. Compared with existing algorithms dealing with 9 emotions, the proposed method enhanced the accuracy rate by 8%. Moreover, experiment shows that the proposed system outperforms similar approaches discriminating between 3 and 4 emotions only. We also found that the proposed method works well, even when applying conventional classification algorithms.

Independent Components of EEG Activity Correlating with Emotional State

Among brain-computer interface studies, electroencephalography (EEG)-based emotion recognition is receiving attention and some studies have performed regression analyses to recognize small-scale emotional changes; however, effective brain regions in emotion regression analyses have not been identified yet. Accordingly, this study sought to identify neural activities correlating with emotional states in the source space. We employed independent component analysis, followed by a source localization method, to obtain distinct neural activities from EEG signals. After the identification of seven independent component (IC) clusters in a k-means clustering analysis, group-level regression analyses using frequency band power of the ICs were performed based on Russell's valence-arousal model. As a result, in the regression of the valence level, an IC cluster located in the cuneus predicted both high- and low-valence states and two other IC clusters located in the left precentral gyrus and the precuneus predicted the low-valence state. In the regression of the arousal level, the IC cluster located in the cuneus predicted both high- and low-arousal states and two posterior IC clusters located in the cingulate gyrus and the precuneus predicted the high-arousal state. In this proof-of-concept study, we revealed neural activities correlating with specific emotional states across participants, despite individual differences in emotional processing.

Quantification of anticipation of excitement with a three-axial model of emotion with EEG

OBJECTIVE

Multiple facets of human emotion underlie diverse and sparse neural mechanisms. Among the many existing models of emotion, the two-dimensional circumplex model of emotion is an important theory. The use of the circumplex model allows us to model variable aspects of emotion; however, such momentary expressions of one's internal mental state still lacks a notion of the third dimension of time. Here, we report an exploratory attempt to build a three-axis model of human emotion to model our sense of anticipatory excitement, 'Waku-Waku' (in Japanese), in which people predictively code upcoming emotional events.

APPROACH

Electroencephalography (EEG) data were recorded from 28 young adult participants while they mentalized upcoming emotional pictures. Three auditory tones were used as indicative cues, predicting the likelihood of the valence of an upcoming picture: positive, negative, or unknown. While seeing an image, the participants judged its emotional valence during the task and subsequently rated their subjective experiences on valence, arousal, expectation, and Waku-Waku immediately after the experiment. The collected EEG data were then analyzed to identify contributory neural signatures for each of the three axes.

MAIN RESULTS

A three-axis model was built to quantify Waku-Waku. As expected, this model revealed the considerable contribution of the third dimension over the classical two-dimensional model. Distinctive EEG components were identified. Furthermore, a novel brain-emotion interface was proposed and validated within the scope of limitations.

SIGNIFICANCE

The proposed notion may shed new light on the theories of emotion and support multiplex dimensions of emotion. With the introduction of the cognitive domain for a brain-computer interface, we propose a novel brain-emotion interface. Limitations of the study and potential applications of this interface are discussed.

Towards a comparative science of emotion: Affect and consciousness in humans and animals

The componential view of human emotion recognises that affective states comprise conscious, behavioural, physiological, neural and cognitive elements. Although many animals display bodily and behavioural changes consistent with the occurrence of affective states similar to those seen in humans, the question of whether and in which species these are accompanied by conscious experiences remains controversial. Finding scientifically valid methods for investigating markers for the subjective component of affect in both humans and animals is central to developing a comparative understanding of the processes and mechanisms of affect and its evolution and distribution across taxonomic groups, to our understanding of animal welfare, and to the development of animal models of affective disorders. Here, contemporary evidence indicating potential markers of conscious processing in animals is reviewed, with a view to extending this search to include markers of conscious affective processing. We do this by combining animal-focused approaches with investigations of the components of conscious and non-conscious emotional processing in humans, and neuropsychological research into the structure and functions of conscious emotions.

EEG correlates of sensorimotor processing: independent components involved in sensory and motor processing

Sensorimotor processing is a critical function of the human brain with multiple cortical areas specialised for sensory recognition or motor execution. Although there has been considerable research into sensorimotor control in humans, the steps between sensory recognition and motor execution are not fully understood. To provide insight into brain areas responsible for sensorimotor computation, we used complex categorization-response tasks (variations of a Stroop task requiring recognition, decision-making, and motor responses) to test the hypothesis that some functional modules are participating in both sensory as well as motor processing. We operationalize functional modules as independent components (ICs) yielded by an independent component analysis (ICA) of EEG data and measured event-related responses by means of inter-trial coherence (ITC). Our results consistently found ICs with event-related ITC responses related to both sensory stimulation and motor response onsets (on average 5.8 ICs per session). These findings reveal EEG correlates of tightly coupled sensorimotor processing in the human brain, and support frameworks like embodied cognition, common coding, and sensorimotor contingency that do not sequentially separate sensory and motor brain processes.

Affective Norms for 4900 Polish Words Reload (ANPW_R): Assessments for Valence, Arousal, Dominance, Origin, Significance, Concreteness, Imageability and, Age of Acquisition

In studies that combine understanding of emotions and language, there is growing demand for good-quality experimental materials. To meet this expectation, a large number of 4905 Polish words was assessed by 400 participants in order to provide a well-established research method for everyone interested in emotional word processing. The Affective Norms for Polish Words Reloaded (ANPW_R) is designed as an extension to the previously introduced the ANPW dataset and provides assessments for eight different affective and psycholinguistic measures of Valence, Arousal, Dominance, Origin, Significance, Concreteness, Imageability, and subjective Age of Acquisition. The ANPW_R is now the largest available dataset of affective words for Polish, including affective scores that have not been measured in any other dataset (concreteness and age of acquisition scales). Additionally, the ANPW_R allows for testing hypotheses concerning dual-mind models of emotion and activation (origin and subjective significance scales). Participants in the current study assessed all 4905 words in the list within 1 week, at their own pace in home sessions, using eight different Self-assessment Manikin (SAM) scales. Each measured dimension was evaluated by 25 women and 25 men. The ANPW_R norms appeared to be reliable in split-half estimation and congruent with previous normative studies in Polish. The quadratic relation between valence and arousal was found to be in line with previous findings. In addition, nine other relations appeared to be better described by quadratic instead of linear function. The ANPW_R provides well-established research materials for use in psycholinguistic and affective studies in Polish-speaking samples.

Emotional Reactivity to Visual Content as Revealed by ERP Component Clustering

Abstract. In the study, the neural basis of emotional reactivity was investigated. Reactivity was operationalized as the impact of emotional pictures on the self-reported ongoing affective state. It was used to divide the subjects into high- and low-responders groups. Independent sources of brain activity were identified, localized with the DIPFIT method, and clustered across subjects to analyse the visual evoked potentials to affective pictures. Four of the identified clusters revealed effects of reactivity. The earliest two started about 120 ms from the stimulus onset and were located in the occipital lobe and the right temporoparietal junction. Another two with a latency of 200 ms were found in the orbitofrontal and the right dorsolateral cortices. Additionally, differences in pre-stimulus alpha level over the visual cortex were observed between the groups. The attentional modulation of perceptual processes is proposed as an early source of emotional reactivity, which forms an automatic mechanism of affective control. The role of top-down processes in affective appraisal and, finally, the experience of ongoing emotional states is also discussed.