Valence Low Arousal Research Articles

EEG emotion recognition based on an innovative information potential index.

The recent exceptional demand for emotion recognition systems in clinical and non-medical applications has attracted the attention of many researchers. Since the brain is the primary object of understanding emotions and responding to them, electroencephalogram (EEG) signal analysis is one of the most popular approaches in affect classification. Previously, different approaches have been presented to benefit from brain connectivity information. We envisioned analyzing the interactions between brain electrodes with the information potential and providing a new index to quantify the connectivity matrix. The current study proposed a simple measure based on the cross-information potential between pairs of EEG electrodes to characterize emotions. This measure was tested for different EEG frequency bands to realize which EEG waves could be fruitful in recognizing emotions. Support vector machine and k-nearest neighbor (kNN) were implemented to classify four emotion categories based on two-dimensional valence and arousal space. Experimental results on the Database for Emotion Analysis using Physiological signals revealed a maximum accuracy of 90.14%, a sensitivity of 89.71%, and an F-score of 94.57% using kNN. The gamma frequency band obtained the highest recognition rates. Furthermore, low valence-low arousal was classified more effectively than other classes.

A hybrid sequential forward channel selection method for enhancing EEG-Based emotion recognition

ABSTRACT In recent times, EEG-based emotion recognition has gained significant attention in affective computing. One of the major challenges in designing an efficient EEG-based emotion-recognition framework lies in handling the high dimensionality of the data recorded from a large number of EEG electrodes. This paper develops a hybrid sequential forward channel selection (HSFCSER) based emotion-recognition method, which aims to identify the most relevant subset of EEG channels and thus reduces the dimensionality of data. In HSFCSER, the Fisher score is used as the filter method, and the wrapper method includes Support Vector Machine (SVM). Wavelet-based features are extracted from the selected optimal EEG channels, followed by the feature selection using a multi-objective genetic algorithm. The proposed method is evaluated on the DEAP database. The methodology identifies 1) two classes of emotions viz. Low/High Valence with an average accuracy of 91.25 ± 5.48% for subject-dependent and 89.38 ± 4.76% for subject-independent, and Low/High Arousal with an average accuracy of 89.92 ± 5.81% for subject-dependent and 86.32 ± 5.34% for subject-independent, and 2) four classes of emotions viz. High Valence-Low Arousal (HVLA), High Valence-High Arousal, Low Valence-Low Arousal, and Low Valence-High Arousal with 75.94 ± 11.07% for subject-dependent and 73.13 ± 8.17% for subject-independent. The efficient electrode positions for emotion recognition are also depicted. The reported results are better compared to the existing results in the literature. The source code of the proposed work is made available at https://github.com/shyammarjit/HSFCS.

Effect of emotion and workload on expertise in programming

In recent times, few studies on program comprehension have incorporated the use of psycho-physiological devices and self-report tools to measure participants’ emotions and cognitive workload. The use of self-report scales is beneficial in settings where there is no access to biosensors or a need for a general understanding of programmers’ emotions and workload during learning or in an assessment of a large group. Hence, we designed a study that would use self-report tools — the Self-Assessment Manikin (SAM) test and NASA TLX (Task Load Index) to obtain the emotions and workload of the participants on two types of programming tasks (logical and conceptual). Our aim was to identify the relationship between emotions and expertise, and workload and expertise on conceptual and logical questions in timed and untimed conditions. Our findings indicate that participants showed Low Valence-High Arousal (LVHA) in timed conditions and High Valence-Low Arousal (HVLA) in untimed conditions. Findings further indicate that high performers showed High Valence-Low Arousal (HVLA) on conceptual questions and High Valence-High Arousal (HVHA) on logical questions, whereas low performers showed High Valence-Low Arousal (HVLA) on conceptual questions and Low Valence-High Arousal (LVHA) on logical questions. We found that participants had a higher workload in timed conditions than untimed conditions and had a higher workload on logical questions than conceptual questions. Furthermore, we employed machine learning algorithms to predict expertise using emotions and workload and the best classifier produced an accuracy of 71.4%, with 72% weighted precision, 71.5% weighted recall, and 71.11% weighted F-score.

CNN and LSTM-Based Emotion Charting Using Physiological Signals.

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence—High Arousal, High Valence—Low Arousal, Low Valence—High Arousal, and Low Valence—Low Arousal. Emotion elicitation average accuracy of is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.

Development of emotion classifier based on absolute and differential attributes of averaged signals of visually stimulated event related potentials

Analysis and study of abstract human relations have always posed a daunting challenge for technocrats engaged in the field of psychometric analysis. The study on emotion recognition is all the more demanding as it involves integration of abstract phenomenon of emotion causation and emotion appraisal through physiological and brain signals. This paper describes the classification of human emotions into four classes, namely: low valence high arousal (LVHA), high valence high arousal (HVHA), high valence low arousal (HVLA) and low valence low arousal (LVLA) using Electroencephalogram (EEG) signals. The EEG signals have been collected on three EEG electrodes along the central line viz: Fz, Cz and Pz. The analysis has been done on average event related potentials (ERPs) and difference of average ERPs using Support Vector Machine (SVM) polynomial classifier. The four-class classification accuracy of 75% using average ERP attributes and an accuracy of 76.8% using difference of ERPs as attributes has been obtained. The accuracy obtained using differential average ERP attributes is better as compared with the already existing studies.

Emotion Recognition from Physiological Signals Using Parallel Stacked Autoencoders

The problem of recognition of emotions in humans is still open from a few aspects. In our study, we used a deep learning method named stacked autoencoder to classify regions of four emotional states in valencearousal plane (high valence-low arousal, high valence-high arousal, low valence-low arousal, and low valence-high arousal). We used a number of physiological signals, including electroencephalogram (EEG), electromyogram (EMG), and other peripheral signals from the DEAP database and extracted spectral and time features from these signals. Also, nonlinear features were extracted from EEG. Then these features were imported to multiple stacked autoencoders in a parallel form (PSAE) to primarily classify four emotional regions in a valence-arousal plane. The final decision about classification was performed using the majority voting method. The average accuracy for classifying four emotional regions from the valence arousal plane, i.e., low arousal and low valence (LALV), low arousal and high valence (LAHV), high arousal and high valence (HAHV), and high arousal and low valence (HALV), reached 93.6%. This result shows that our proposed method demonstrates certain advantages in solving the classification problem; probably, it can also be used for other classification problems.

Analysis of EEG entropy during visual evocation of emotion in schizophrenia

BackgroundIn this study, the international affective picture system was used to evoke emotion, and then the corresponding signals were collected. The features from different points of brainwaves, frequency, and entropy were used to identify normal, moderately, and markedly ill schizophrenic patients.MethodsThe signals were collected and preprocessed. Then, the signals were separated according to three types of emotions and five frequency bands. Finally, the features were calculated using three different methods of entropy. For classification, the features were divided into different sections and classification using support vector machine (principal components analysis on 95%). Finally, simple regression and correlation analysis between the total scores of positive and negative syndrome scale and features were used.ResultsAt first, we observed that to classify normal and markedly ill schizophrenic patients, the identification result was as high as 81.5%, and therefore, we further explored moderately and markedly ill schizophrenic patients. Second, the identification rate in both moderately and markedly ill schizophrenic patient was as high as 79.5%, which at the Fz point signal in high valence low arousal fragments was calculated using the ApEn methods. Finally, the total scores of positive and negative syndrome scale were used to analyze the correlation with the features that were the five frequency bands at the Fz point signal. The results show that the p value was less than .001 at the beta wave in the 15–18 Hz frequency range.

Intensifying Emotional Reactions via Tactile Gestures in Immersive Films

The film industry continuously strives to make visitors’ movie experience more immersive and thus, more captivating. This is realized through larger screens, sophisticated speaker systems, and high quality 2D and 3D content. Moreover, a recent trend in the film industry is to incorporate multiple interaction modalities, such as 4D film, to simulate rain, wind, vibration, and heat, in order to intensify viewers’ emotional reactions. In this context, humans’ sense of touch possesses significant potential for intensifying emotional reactions for the film experience beyond audio-visual sensory modalities. This article presents a framework for authoring tactile cues (tactile gestures as used in this article) and enabling automatic rendering of said gestures to intensify emotional reactions in an immersive film experience. To validate the proposed framework, we conducted an experimental study where tactile gestures are designed and evaluated for the ability to intensify four emotional reactions: high valence-high arousal, high valence-low arousal, low valence-high arousal, and low valence-low arousal. Using a haptic jacket, participants felt tactile gestures that are synchronized with the audio-visual contents of a film. Results demonstrated that (1) any tactile feedback generated a positive user experience; (2) the tactile feedback intensifies emotional reactions when the audio-visual stimuli elicit clear emotional responses, except for low arousal emotional response since tactile gestures seem to always generate excitement; (3) purposed tactile gestures do not seem to significantly outperform randomized tactile gesture for intensifying specific emotional reactions; and (4) using a haptic jacket is not distracting for the users.

The facial expression of schizophrenic patients applied with infrared thermal facial image sequence

BackgroundSchizophrenia is a neurological disease characterized by alterations to patients’ cognitive functions and emotional expressions. Relevant studies often use magnetic resonance imaging (MRI) of the brain to explore structural differences and responsiveness within brain regions. However, as this technique is expensive and commonly induces claustrophobia, it is frequently refused by patients. Thus, this study used non-contact infrared thermal facial images (ITFIs) to analyze facial temperature changes evoked by different emotions in moderately and markedly ill schizophrenia patients.MethodsSchizophrenia is an emotion-related disorder, and images eliciting different types of emotions were selected from the international affective picture system (IAPS) and presented to subjects during ITFI collection. ITFIs were aligned using affine registration, and the changes induced by small irregular head movements were corrected. The average temperatures from the forehead, nose, mouth, left cheek, and right cheek were calculated, and continuous temperature changes were used as features. After performing dimensionality reduction and noise removal using the component analysis method, multivariate analysis of variance and the Support Vector Machine (SVM) classification algorithm were used to identify moderately and markedly ill schizophrenia patients.ResultsAnalysis of five facial areas indicated significant temperature changes in the forehead and nose upon exposure to various emotional stimuli and in the right cheek upon evocation of high valence low arousal (HVLA) stimuli. The most significant P-value (lower than 0.001) was obtained in the forehead area upon evocation of disgust. Finally, when the features of forehead temperature changes in response to low valence high arousal (LVHA) were reduced to 9 using dimensionality reduction and noise removal, the identification rate was as high as 94.3%.ConclusionsOur results show that features obtained in the forehead, nose, and right cheek significantly differed between moderately and markedly ill schizophrenia patients. We then chose the features that most effectively distinguish between moderately and markedly ill schizophrenia patients using the SVM. These results demonstrate that the ITFI analysis protocol proposed in this study can effectively provide reference information regarding the phase of the disease in patients with schizophrenia.

Visualizing the brain connectivity during negative emotion processing– An EEG study

Visualizing the brain connectivity during negative emotion processing– An EEG study

Personality effects and sex differences on the International Affective Picture System (IAPS): A Spanish and Swiss study

The present study analyses the relationship between Anxiety and Impulsivity personality factors and emotions, by controlling for country and sex effects in a sample of Spanish and Swiss university students. Emotions were assessed through the International Affective Picture System (IAPS) of pictures (valence/arousal) using the Self-Assessment Manikin (SAM) procedure. The mixed valence/arousal groups’ pictures were formed according to Tok, Koyuncu, Dural and Catikkas procedure (2010). Results showed that females scored significantly higher in Anxiety factor and men in Impulsivity factor in both countries. The effect of sex was highly significant for Anxiety (ŋ2: 0.12), but there was no significant effect of the country. Also, females obtained higher scores in the four valence/arousal pictures groups. The sex effect was particularly robust for negative valence–high arousal (ŋ2: 0.13). Impulsivity correlated with high ratings of positive valence–high arousal while Anxiety correlated with high ratings of negative valence–high arousal and with high ratings of negative valence–low arousal in both sexes, although scores were higher for females. Structural Equation Modelling confirmed these relationships. Nevertheless, Anxiety and Impulsivity explained only a small amount of the accounted variance of the self-reported valence and arousal of the pictures.