Classification Of Affective States Research Articles

“It's wrong to relax, you have to be on the go, go, go, all the time”: Emotional transformation in a case of emotion‐focused therapy for generalised anxiety disorder

AbstractGeneralised anxiety disorder (GAD) is a highly prevalent, chronic and impairing disorder. The aim of this study was to qualitatively describe the transformation in emotional processing across 16 sessions of a successful emotion‐focused therapy (EFT) treatment for GAD. A theoretical framework derived from the EFT model of in‐session emotional transformation proposed by Pascual‐Leone and Greenberg was used as the basis for qualitative analysis. Additionally, the Classification of Affective Meaning States, the Client‐Expressed Arousal Scale‐III and the Client Emotional Productivity Scale‐Revised were utilised by observer–raters to classify moment‐by‐moment shifts in client affective‐meaning states, emotional arousal and emotional productivity. Results supported the emotion transformation model. The client presented in a state of poorly differentiated but highly aroused distress provoked by identifiable interpersonal and intrapersonal triggers. Emotional and behavioural avoidance provoked by fear of pain was evident. Accessing core pain and responding to attendant needs with compassion and protective anger was shown to facilitate the higher order emotional processing states of grief, relief and agency. Implications are proposed, including that addressing core emotional vulnerability may be a critical intervention in the successful treatment of GAD.

Decoding auditory-evoked response in affective states using wearable around-ear EEG system

Objective. In this paper, an around-ear EEG system is investigated as an alternative methodology to conventional scalp-EEG-based systems in classifying human affective states in the arousal-valence domain evoked in response to auditory stimuli. Approach. EEG recorded from around the ears is compared to EEG collected according to the international 10–20 system in terms of efficacy in an affective state classification task. A wearable device with eight dry EEG channels is designed for ear-EEG acquisition in this study. Twenty-one subjects participated in an experiment consisting of six sessions over three days using both ear and scalp-EEG acquisition methods. Experimental tasks consisted of listening to an auditory stimulus and self-reporting the elicited emotion in response to the said stimulus. Various features were used in tandem with asymmetry methods to evaluate binary classification performances of arousal and valence states using ear-EEG signals in comparison to scalp-EEG. Main results. We achieve an average accuracy of 67.09% ± 6.14 for arousal and 66.61% ± 6.14 for valence after training a multi-layer extreme learning machine with ear-EEG signals in a subject-dependent context in comparison to scalp-EEG approach which achieves an average accuracy of 68.59% ± 6.26 for arousal and 67.10% ± 4.99 for valence. In a subject-independent context, the ear-EEG approach achieves 63.74% ± 3.84 for arousal and 64.32% ± 6.38 for valence while the scalp-EEG approach achieves 64.67% ± 6.91 for arousal and 64.86% ± 5.95 for valence. The best results show no significant differences between ear-EEG and scalp-EEG signals for classifications of affective states. Significance. To the best of our knowledge, this paper is the first work to explore the use of around-ear EEG signals in emotion monitoring. Our results demonstrate the potential use of around-ear EEG systems for the development of emotional monitoring setups that are more suitable for use in daily affective life log systems compared to conventional scalp-EEG setups.

Affective states classification performance of audio-visual stimuli from EEG signals with multiple-instance learning

Throughout various disciplines, emotion recognition continues to be an essential subject of study. With the advancement of machine learning methods, accurate emotion recognition from different data modalities (facial images, brain EEG signals) has become possible. Success of EEG-based emotion recognition systems depends on efficient feature extraction and pre/postprocessing of signals. Main objective of this study is to analyze the efficacy of multiple-instance learning (MIL) on postprocessing features of EEG signals using three different domains (time, frequency, time-frequency) for human emotion classification. Methods and results are presented for single-trial classification of valence (V), arousal (A), and dominance (D) ratings from EEG signals obtained with audio (A), video (V), and audio-video (AV) stimulus using alpha, beta and gamma bands. High accuracy was observed with both binary and multiclass classification of the AV stimulus. Findings in this study suggest that MIL applied on frequency features yields efficient results on EEG emotion recognition.

Applying Big Transfer-based classifiers to the DEAP dataset.

Affective brain-computer interfaces are a fast-growing area of research. Accurate estimation of emotional states from physiological signals is of great interest to the fields of psychology and human-computer interaction. The DEAP dataset is one of the most popular datasets for emotional classification. In this study we generated heat maps from spectral data within the neurological signals found in the DEAP dataset. To account for the class imbalance within this dataset, we then discarded images belonging to the larger class. We used these images to fine-tune several Big Transfer neural networks for binary classification of arousal, valence, and dominance affective states. Our best classifier was able to achieve greater than 98% accuracy and 990% balanced accuracy in all three classification tasks. We also investigated the effects of this balancing method on our classifiers.

Does emotional processing predict 18-month post-therapy outcomes in the experiential treatment of major depression?

Objective: This study tested whether emotion-focused therapy’s (EFT) emotion processing theory serves as a predictor of 18-month post-therapy outcomes for major depressive disorder (MDD), independent of experiential therapy received. Method: We examined sequences of emotion episodes using the THEME™ sequential analysis of emotional processing in 55 clients who provided 18-month post-therapy Beck Depression Inventory reports after receiving experiential treatment in the York I and II trials, either emotion-focused or client-centered therapy. Archival Classification of Affective Meaning States (CAMS) ratings of emotion episodes of clients’ working-phase sessions were analyzed using THEME™ sequential analyses of emotions coded during emotion episodes. Results: According to THEME™, poor outcome clients (Beck Depression Inventory at 18 months ≥ 10) expressed more emotion episode sequences containing secondary, or self-protective emotions, than good outcome clients. Good outcome clients expressed more emotion sequences with needs, hurt/grief, and assertive anger than poor outcome clients. Conclusions: EFT sequential emotional processing theory appears to offer good basic assumptions for experiential long-term therapy outcomes after receiving therapy for MDD. Generalization of the theory for other treatments is desired.

Driver Drowsiness Detection: An Approach Based on Intelligent Brain–Computer Interfaces

Estimating reaction times (RTs) and drowsiness states from brain signals is a notable step in creating passive brain–computer interfaces (BCIs). Prior to the deep learning era, estimating RTs and drowsiness from electroencephalogram (EEG) signals was feasible only with moderate accuracy, which led to unreliability for neuro-engineering applications. However, recent developments in machine learning algorithms, notably stationarity-based approaches and deep convolutional neural networks (CNNs), have demonstrated promising results for a class of BCI systems, e.g., motor imagery BCIs, and affective state classification. These methods have not been systematically analyzed for EEG-based driver drowsiness detection and RT prediction.

Ecological momentary assessment of emotional processing: An exploratory analysis comparing daily life and a psychotherapy analogue session

AbstractBackgroundEmotional processing has been studied in psychotherapy as a state‐dependent, sequential process of change. So far, no studies have applied this conceptualisation of emotional processing to the assessments of emotion in daily life. This is particularly important in the light of the pertinence of day‐by‐day fluctuations of emotions for understanding mental health and for monitoring the impact of prevention and psychotherapy programmes. This study examined the internal and ecological validity of a state‐dependent conceptualisation of emotional processing in daily life, in comparison with an experiential‐psychodynamic psychotherapy analogue session.MethodsIn total, N = 42 university students participated in an experiential‐psychodynamic session, completed symptom measures and responded to a one‐week period of ecological momentary assessment (EMA) using a smartphone. Emotional processing in the session was assessed using the valid observer‐rated measure Classification of Affective Meaning States (CAMS), and emotional responses in daily life were assessed using newly developed theory‐consistent items self‐rated via an interactive smartphone program.ResultsInternal validity was generally satisfactory across the subscales used in EMA. Correspondence between EMA and in‐session emotional processing was generally low, but specific relationships were found between self‐rated fear, rejecting anger, hurt/grief or loneliness and the observer‐rated productive emotions in the psychotherapy analogue session. Relationships between maladaptive emotional processing and intensity in symptoms were found.ConclusionsThis is the first study to have examined the validity of a state‐dependent conception of emotional processing in daily life, in direct comparison with a psychological session. We recommend using this assessment schedule to develop or complement integrative prevention or intervention programmes.

Happy, Nervous or Surprised? Classification of Human Affective States in Social Media

Sentiment classification has been a well-investigated research area in the computational linguistics community. However, most of the research is primarily focused on detecting simply the polarity in text, often needing extensive manual labeling of ground truth. Additionally, little attention has been directed towards a finer analysis of human moods and affective states. Motivated by research in psychology, we propose and develop a classifier of several human affective states in social media. Starting with about 200 moods, we utilize mechanical turk studies to derive naturalistic signals from posts shared on Twitter about a variety of affects of individuals. This dataset is then deployed in an affect classification task with promising results. Our findings indicate that different types of affect involve different emotional content and usage styles; hence the performance of the classifier on various affects can differ considerably.

Development of Unified Neuro-Affective Classification Tool (UNACT)

Brain signals have been analysed to understand the affective state of different cognitive and mental conditions. For example, through the analysis, we can visualize the changes of emotion while driving, identify an autistic kid, understand the conditions that stimulate attention while studying, and many more, because emotion has a strong impact on cognitive processes in humans’ activities. This can be done through a machine learning technique, which includes data acquisition, pre-processing, feature extraction, and training. However, no existing tool integrates all supervised machine learning processes for affective state classification, which makes the process tedious and time-consuming for an analyst by doing programming. Therefore, this project aims to develop a brain analysis tool, namely Unified Neuro-Affective Classification Tool (UNACT). It consists of 3 main functions including training, classifying, and analysis. In the study of affective state electroencephalogram (EEG) signals have used, which measures brain signals. UNACT uses the Butterworth Bandpass filter for EEG signal filtering, the Power Spectral Density method for feature extraction, and the Multi-layer perceptron (MLP) for emotion classification. This tool can be used by a non-technical person to perform affective-emotional state analysis without having programming knowledge.

Working with emotion predicts sudden gains during experiential therapy for depression

Objective: This study involves the first attempt to identify sudden gains in a sample of clients undergoing experiential therapy for depression while also investigating client and therapist change processes related to sudden gains. Method: Pre- and post-session Beck Depression Inventory, short form (BDI-SF) questionnaires were used to identify sudden gains and differentiate between in-session and between-session symptom changes in thirty-six client-therapist dyads. Archival videotape data of a sub-sample were coded using the Experiencing Scale, Classification of Affective Meaning States, and the Coding System for Therapist Focus. Results: The study revealed that 63.9% of clients experienced a sudden gain and the bulk of the total mean symptom decrease (74.9%) was found to occur within the session preceding the sudden gain. During this critical session, clients were more likely to display deepened experiencing (p < .01, η 2 = .34), clients were more likely to express “primary adaptive emotions” (p < .05, r = .38), and therapists were found to be more likely to focus on unmet client needs (p < .01, d = .75). Conclusion: The majority of the sudden gain change occurs within session in experiential therapy, and primary adaptive emotions as well as addressing unmet needs are possible processes of sudden gains.

Self-Supervised ECG Representation Learning for Emotion Recognition

We exploit a self-supervised deep multi-task learning framework for electrocardiogram (ECG) -based emotion recognition. The proposed solution consists of two stages of learning a) learning ECG representations and b) learning to classify emotions. ECG representations are learned by a signal transformation recognition network. The network learns high-level abstract representations from unlabeled ECG data. Six different signal transformations are applied to the ECG signals, and transformation recognition is performed as pretext tasks. Training the model on pretext tasks helps the network learn spatiotemporal representations that generalize well across different datasets and different emotion categories. We transfer the weights of the self-supervised network to an emotion recognition network, where the convolutional layers are kept frozen and the dense layers are trained with labelled ECG data. We show that the proposed solution considerably improves the performance compared to a network trained using fully-supervised learning. New state-of-the-art results are set in classification of arousal, valence, affective states, and stress for the four utilized datasets. Extensive experiments are performed, providing interesting insights into the impact of using a multi-task self-supervised structure instead of a single-task model, as well as the optimum level of difficulty required for the pretext self-supervised tasks.

CDNB: CAVIAR-Dragonfly Optimization with Naive Bayes for the Sentiment and Affect Analysis in Social Media.

With the advent of the new information technologies, the growth of online reviews regarding an organization or a company or any other sector has been playing a vital role in improving the sector plans and decisions. The vast significance of the online reviews that determine the sentiment polarity is the hectic challenge of the current scenario. Sentiment classification is a process of classifying the text according to the sentimental polarities of opinions, which has positive or negative. Thus, this article concentrates on presenting a novel method, named CAVIAR-Dragonfly optimization with Extended Naive Bayes (CDNB), for performing sentiment classification and affective state classification. At first, the BITS review from Twitter is subjected to preprocessing, which includes stop word removal and stemming. Then, the next step is the feature extraction, in which all the reviews are converted to a feature vector. After that, all the individual feature vectors are collected to form the feature matrix, which is applied to the proposed C-Dragonfly optimization algorithm, to perform the sentiment classification and affective state classification. The performance of the proposed method is analyzed using the Twitter Sentiment Analysis Training Corpus Data Set based on true positive rate (TPR), true negative rate (TNR), and accuracy. From the analysis, it can be shown that the proposed method yields the maximum TPR, TNR, and accuracy of 89.0934%, 72.3064%, and 79.3591% for sentiment classification and 84.2122%, 66.2187%, and 76.6249% for the sentiment affective state classification.

Modern Machine-Learning Algorithms: For Classifying Cognitive and Affective States From Electroencephalography Signals

Estimating cognitive or affective states from brain signals is a key but challenging step in creating passive brain-computer interface (BCI) applications. So far, estimating mental workloads or emotions from electroencephalogram (EEG) signals is only feasible with modest classification accuracies, which thus lead to unreliable neuroadaptive applications. However, recent machine-learning algorithms, notably Riemannian geometry-based classifiers (RGCs) and convolutional neural networks (CNNs), have shown promise for other BCI systems, e.g., motor imagery BCIs. However, they have not been formally studied and compared for cognitive or affective states classification.

Impact of inquiry interventions on students in e-learning and classroom environments using affective computing framework

Effective teaching strategies improve the students’ learning rate within academic learning time. Inquiry-based instruction is one of the effective teaching strategies used in the classrooms. But these teaching strategies are not adapted in other learning environments like intelligent tutoring systems, including auto tutors. In this paper, we propose an automatic inquiry-based instruction teaching strategy, i.e., inquiry intervention using students’ affective states. The proposed model contains two modules: the first module consists of the proposed framework for predicting the unobtrusive multi-modal students’ affective states (teacher-centric attentive and in-attentive states) using the facial expressions, hand gestures and body postures. The second module consists of the proposed automated inquiry-based instruction teaching strategy to compare the learning outcomes with and without inquiry intervention using affective state transitions for both an individual and a group of students. The proposed system is tested on four different learning environments, namely: e-learning, flipped classroom, classroom and webinar environments. Unobtrusive recognition of students’ affective states is performed using deep learning architectures. After student-independent tenfold cross-validation, we obtained the students’ affective state classification accuracy of 77% and object localization accuracy of 81% using students’ faces, hand gestures and body postures. The overall experimental results demonstrate that there is a positive correlation with $$r=0.74$$ between students’ affective states and their performance. Proposed inquiry intervention improved the students’ performance as there is a decrease of 65%, 43%, 43%, and 53% in overall in-attentive affective state instances using the inquiry interventions in e-learning, flipped classroom, classroom and webinar environments, respectively.

Emotional processing of trauma narratives is a predictor of outcome in emotion-focused therapy for complex trauma.

This study tested a model of emotional processes over the course of emotion-focused therapy for trauma. The model of emotional processing (Pascual-Leone & Greenberg, 2007) proposes a sequential order of shifting from "early expressions of distress" to "primary adaptive emotion" that aid in adaptive functioning. Thirty-eight participants were taken from a randomized clinical trial to examine in-session process from video recordings of treatment. The sample had an average age (M = 44.3 years) and the majority was female (55.3%) and of European descent (89.5%). The Classification of Affective Meaning States was used to examine changes in emotional processes during trauma narratives in both early and late sessions. Processes were related to treatment outcome as measured by the Impact of Event Scale and the Resolution Scale. Sessions later in treatment showed a higher frequency of primary adaptive emotions compared with early sessions (p < .001, r = .76). Primary adaptive emotions were also more frequent in good-outcome cases (p = .017). Using emotional processes as predictors correctly classified 67% of poor-outcome cases and 80% of good-outcome cases. Increases in primary adaptive emotions from early to late treatment sessions more than doubled the odds (2.2) of having a good treatment outcome. The order of emotion was concordant with the sequential model in early sessions and partially concordant in late sessions. Findings have implications for guiding therapeutic process in a productive manner that leads to trauma recovery. Changes in discrete emotions were related to good treatment outcome. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

A Portable Personality Recognizer Based on Affective State Classification Using Spectral Fusion of Features

In this paper, we introduce a system named Portable Personality Recognizer (PPR), which classifies the personality of an individual using his/her transitions of affective states. This work attempts to reveal the latent relationship between emotions and personality of a person. Here, we train a hidden Markov model (HMM) with observable emotional states viz. Happiness (H), Anger (A), Surprise (S) and Disgust (D) and the hidden traits viz. Psychoticism (P), Extraversion (E) and Neuroticism (N). Based on the model, the system estimates the personality as Psychotic, Extravert or Neurotic. It does so by capturing the facial images of an individual using a visible and a thermal camera to decide the present affective state of the person. The emotion classification is carried out using fused eigenfeatures from the visible and blood perfused thermal images. The emotional state changes are observed using the trained HMM to estimate the personality. The proposed hardware prototype consists of a Banana Pi board with a seven inch LCD screen having a thermal and a visible camera add-ons. The system achieves an emotion classification accuracy of 87.145 percent, while an accuracy of 87.87 percent is achieved for personality recognition.

Classification of Affective States via EEG and Deep Learning

Human emotions play a key role in numerous decision-making processes. The ability to correctly identify likes and dislikes as well as excitement and boredom would facilitate novel applications in neuromarketing, affective entertainment, virtual rehabilitation and forensic neuroscience that leverage on sub-conscious human affective states. In this neuroinformatics investigation, we seek to recognize human preferences and excitement passively through the use of electroencephalography (EEG) when a subject is presented with some 3D visual stimuli. Our approach employs the use of machine learning in the form of deep neural networks to classify brain signals acquired using a brain-computer interface (BCI). In the first part of our study, we attempt to improve upon our previous work, which has shown that EEG preference classification is possible although accuracy rates remain relatively low at 61%-67% using conventional deep learning neural architectures, where the challenge mainly lies in the accurate classification of unseen data from a cohort-wide sample that introduces inter-subject variability on top of the existing intra-subject variability. Such an approach is significantly more challenging and is known as subject-independent EEG classification as opposed to the more commonly adopted but more time-consuming and less general approach of subject-dependent EEG classification. In this new study, we employ deep networks that allow dropouts to occur in the architecture of the neural network. The results obtained through this simple feature modification achieved a classification accuracy of up to 79%. Therefore, this study has shown that the use of a deep learning classifier was able to achieve an increase in emotion classification accuracy of between 13% and 18% through the simple adoption of the use of dropouts compared to a conventional deep learner for EEG preference classification. In the second part of our study, users are exposed to a roller-coaster experience as the emotional stimuli which are expected to evoke the emotion of excitement, while simultaneously wearing virtual reality goggles, which delivers the virtual reality experience of excitement, and an EEG headset, acquires the raw brain signals detected when exposed to this excitement stimuli. Here, a deep learning approach is used to improve the excitement detection rate to well above the 90% accuracy level. In a prior similar study, the use of conventional machine learning approaches involving k-Nearest Neighbour (kNN) classifiers and Support Vector Machines (SVM) only achieved prediction accuracy rates of between 65% and 89%. Using a deep learning approach here, rates of 78%-96% were achieved. This demonstrates the superiority of adopting a deep learning approach over other machine learning approaches for detecting human excitement when immersed in an immersive virtual reality environment.

Mean of Correlation Method for Optimization of Affective States Detection in Children

At the moment, most of the studies on classification of affective states for children focus on visual observations and physiological cues, where all data collection for measuring physiological signals are contact-based and invasive. With the requirement of having the measuring device attached to the body approach, distraction of the subject normally masks the true affective states of the subject due to discomfort. In this paper, a non-invasive, contactless, and less distraction method is proposed to measure the physiological cues of the subjects using their thermal imprints from frontal face imaging. A thermal image camera is used to identify basic affective states, where it is a contactless and seamless device with ability to read the radiated thermal imprint of the subjects’ facial skin temperature. This paper proposes an effective algorithm of texture analysis based on novel technique using Gray Level Co-occurrence Matrix approach to be applied so as to identify blood-flow region. The cues from the first order statistics are computed in the identified blood flow region and concatenated along with second order statistics cues, in order to construct feature vectors to administer the vital and distinguishable characteristic pattern between affective states in thermal images. Result from the fine k-NN classifier obtained promises the efficacy of the proposed approach to be applied in our future work in human–robot interaction for autistic children learning and training.

The role of shame and self‐compassion in psychotherapy for narcissistic personality disorder: An exploratory study

This process-outcome study aims at exploring the role of shame, self-compassion, and specific therapeutic interventions in psychotherapy for patients with narcissistic personality disorder (NPD). This exploratory study included a total of N=17 patients with NPD undergoing long-term clarification-oriented psychotherapy. Their mean age was 39years, and 10 were male. On average, treatments were 64 sessions long (range between 45 and 99). Sessions 25 and 36 were rated using the Classification of Affective Meaning States and the Process-Content-Relationship Scale. Outcome was assessed using the Symptom Check List-90 and Beck Depression Inventory-II. Between Sessions 25 and 36, a small decrease in the frequency of shame was found (d=.30). In Session 36, the presence of self-compassion was linked with a set of specific therapist interventions (process-guidance and treatment of behaviour-underlying assumptions; 51% of variance explained and adjusted). This study points to the possible central role of shame in the therapeutic process of patients with NPD. Hypothetically, one way of resolving shame is, for the patient, to access underlying self-compassion.

Affective State Classification Through CMAC-Based Model of Affects (CCMA) Using SVM

A number of computational models have been proposed to perform emotion profiling through affective state classification using EEG signals. However, such models do not include both temporal and spatial dynamic of the signals. It is also observed that the performance of classifying emotion using the existing models produce high classification accuracy on one subject, but not on different subjects. Thus, in this paper CMAC-based Computational Model of Affects (CCMA) is proposed as feature extraction for the classification task. CCMA keeps the temporal and spatial dynamics of EEG signals to produce better classification performance. Using Support Vector Machine (SVM) as classifier, the features produce higher classification accuracy for heterogeneous test.