An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data

Flow and Physiological Response Assessment during Exercise Using Metrorhythmic Stimuli

Activity and physical effort positively affect a person's psychophysical state and emotional experience. Interest in the phenomenon of flow, the state of perceived arousal, stems from its relationship to an individual's intrinsic motivation. Flow is an area of research in many fields, including sports. Nowadays, solutions are being sought to support the traditional assessment of cognitive and affective states using analysis of physiological signals. Therefore, the present study analysed and estimated the physiological responses that may occur during the induction of a flow state between exercises stimulated by additional metrorhythmic stimuli. Thirty-six healthy subjects participated in the study. The effects of various metrorhythmic stimuli on the body's physiological response during the subjects' free gait were examined. The physiological response and flow intensity were evaluated when the rate of individual stimuli was changed, and the rate was enforced. Several statistically significant variables and correlations were determined for physiological indicators depending on the stage of the study conducted and the level of flow experienced. A positive, statistically significant correlation of flow and HRV frequency variables was obtained. The results also confirm previous literature reports on the relationship between flow response and frequency heart rate variability during physical activity.

Analysis of the relationship between emotion intensity and electrophysiology parameters during a voice examination of opera singers

OBJECTIVES

Emotions and stress affect voice production. There are only a few reports in the literature on how changes in the autonomic nervous system affect voice production. The aim of this study was to examine emotions and measure stress reactions during a voice examination procedure, particularly changes in the muscles surrounding the larynx.

MATERIAL AND METHODS

The study material included 50 healthy volunteers (26 voice workers - opera singers, 24 control subjects), all without vocal complaints. All subjects had good voice quality in a perceptual assessment. The research procedure consisted of 4 parts: an ear, nose, and throat (ENT)‑phoniatric examination, surface electromyography, recording physiological indicators (heart rate and skin resistance) using a wearable wristband, and a psychological profile based on questionnaires.

RESULTS

The results of the study demonstrated that there was a relationship between positive and negative emotions and stress reactions related to the voice examination procedure, as well as to the tone of the vocal tract muscles. There were significant correlations between measures describing the intensity of experienced emotions and vocal tract muscle maximum amplitude of the cricothyroid (CT) and sternocleidomastoid (SCM) muscles during phonation and non-phonation tasks. Subjects experiencing eustress (favorable stress response) had increased amplitude of submandibular and CT at rest and phonation. Subjects with high levels of negative emotions, revealed positive correlations with SCMmax during the glissando. The perception of positive and negative emotions caused different responses not only in the vocal tract but also in the vegetative system. Correlations were found between emotions and physiological parameters, most markedly in heart rate variability. A higher incidence of extreme emotions was observed in the professional group.

CONCLUSIONS

The activity of the vocal tract muscles depends on the type and intensity of the emotions and stress reactions. The perception of positive and negative emotions causes different responses in the vegetative system and the vocal tract. Int J Occup Med Environ Health. 2024;37(1):84-97.

Examining the relationship of personality traits with online teaching using emotive responses and physiological signals

In the education sector, there is a rapid increase in using online teaching and learning scenarios. Making these scenarios more effective is the main purpose of this study. Though there are a lot of factors that affect it, however, the primary focus is to find out the relationship between a teacher's personality and their liking for online teaching. To conduct the study, a framework has been proposed which is a mixed design of self-reported (emotions and personality) data and physiological responses of a teacher. In self-reported data, along with teachers, learners' perception of a teacher's personality is also considered which explores their relationship with online teaching. The final results reveal that teachers with a high level of agreeableness, conscientiousness, and openness personality traits are more comfortable with online teaching as compared to extraversion and neuroticism traits. To validate the self-reported data analysis, the physiological responses of teachers were recorded that ensure the authenticity of the collected data. It also ensures that the physiological responses along with emotions are also good indicators of personality recognition.

Emotion Recognition from Physiological Channels Using Graph Neural Network

In recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The key assumption of the validation was to analyze its correctness for the Circumplex model to further analyze the solution for emotion recognition in the Ekman modal. The novelty of this research is not only the utilization of a GNN network with GraphSleepNet architecture for emotion recognition, but also the analysis of the potential of emotion recognition based on differential entropy features in the Ekman model with a neutral state and a special focus on continuous emotion recognition during the performance of an activity The GNN was validated against the AMIGOS dataset. The research shows how the use of various modalities influences the correctness of the recognition of basic emotions and the neutral state. Moreover, the correctness of the recognition of basic emotions is validated for two configurations of the GNN. The results show numerous interesting observations for Ekman's model while the accuracy of the Circumplex model is similar to the baseline methods.

Using Smartphone App Use and Lagged-Ensemble Machine Learning for the Prediction of Work Fatigue and Boredom

INTRO

As smartphone usage becomes increasingly prevalent in the workplace, the physical and psychological implications of this behavior warrant consideration. Recent research has investigated associations between workplace smartphone use and fatigue and boredom, yet findings are not conclusive.

METHODS

To build off recent efforts, we applied an ensemble machine learning model on a previously published dataset of N = 83 graduate students in the Netherlands to predict work boredom and fatigue from passively collected smartphone app use information. Using time-based feature engineering and lagged variations of the data to train, validate, and test idiographic models, we evaluated the efficacy of a lagged-ensemble predictive paradigm on sparse temporal data. Moreover, we probed the relative importance of both derived app use variables and lags within this predictive framework.

RESULTS

The ability to predict fatigue and boredom trajectories from app use information was heterogeneous and highly person-specific. Idiographic modeling reflected moderate to high correlative capacity (r > 0.4) in 47% of participants for fatigue and 24% for boredom, with better overall performance in the fatigue prediction task. App use relating to duration, communication, and patterns of use frequency were among the most important features driving predictions across lags, with longer lags contributing more heavily to final ensemble predictions compared with shorter ones.

CONCLUSION

A lag- specific ensemble predictive paradigm is a promising approach to leveraging high-dimensional app use behavioral data for the prediction of work fatigue and boredom. Future research will benefit from evaluating associations on densely collected data across longer time scales.

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals

This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.

Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods

The use of imagined speech with electroencephalographic (EEG) signals is a promising field of brain-computer interfaces (BCI) that seeks communication between areas of the cerebral cortex related to language and devices or machines. However, the complexity of this brain process makes the analysis and classification of this type of signals a relevant topic of research. The goals of this study were: to develop a new algorithm based on Deep Learning (DL), referred to as CNNeeg1-1, to recognize EEG signals in imagined vowel tasks; to create an imagined speech database with 50 subjects specialized in imagined vowels from the Spanish language (/a/,/e/,/i/,/o/,/u/); and to contrast the performance of the CNNeeg1-1 algorithm with the DL Shallow CNN and EEGNet benchmark algorithms using an open access database (BD1) and the newly developed database (BD2). In this study, a mixed variance analysis of variance was conducted to assess the intra-subject and inter-subject training of the proposed algorithms. The results show that for intra-subject training analysis, the best performance among the Shallow CNN, EEGNet, and CNNeeg1-1 methods in classifying imagined vowels (/a/,/e/,/i/,/o/,/u/) was exhibited by CNNeeg1-1, with an accuracy of 65.62% for BD1 database and 85.66% for BD2 database.

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

The theory of modern organizations considers emotional intelligence to be the metric for tools that enable organizations to create a competitive vision. It also helps corporate leaders enthusiastically adhere to the vision and energize organizational stakeholders to accomplish the vision. In this study, the one-dimensional convolutional neural network classification model is initially employed to interpret and evaluate shifts in emotion over a period by categorizing emotional states that occur at particular moments during mutual interaction using physiological signals. The self-organizing map technique is implemented to cluster overall organizational emotions to represent organizational competitiveness. The analysis of variance test results indicates no significant difference in age and body mass index for participants exhibiting different emotions. However, a significant mean difference was observed for the blood volume pulse, galvanic skin response, skin temperature, valence, and arousal values, indicating the effectiveness of the chosen physiological sensors and their measures to analyze emotions for organizational competitiveness. We achieved 99.8% classification accuracy for emotions using the proposed technique. The study precisely identifies the emotions and locates a connection between emotional intelligence and organizational competitiveness (i.e., a positive relationship with employees augments organizational competitiveness).

Robust feature collection and classification of network culture

The network provides a convenient mechanism for publishing and obtaining documents, and has now become a gathering place for all kinds of information. In the network, the amount of information increases exponentially, and how to dig useful patterns or knowledge from the massive network culture has become a hot topic for scholars. In data mining, in order to enable readers to quickly obtain the content of interest, research text classification, and automatically classify text data according to a certain classification model. Internet cultural text data has the characteristics of unstructured, subjective, high-dimensional, etc., which makes it difficult for text mining algorithms to extract effective and easy-to-understand classification rules, and the computational complexity is too high. This paper proposes a feature selection method based on robust features, using sample deviation and variance as the criteria for feature attributes to rank the importance of feature attributes, and select the best feature attribute subset. The experimental results show that the classification accuracy of the feature selection method based on sample deviation and variance proposed in this paper is higher than the traditional word frequency as the feature selection method, which proves the feasibility and superiority of the feature selection method proposed in this paper.

EEG-Based Emotion Classification for Alzheimer's Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models

As the number of patients with Alzheimer's disease (AD) increases, the effort needed to care for these patients increases as well. At the same time, advances in information and sensor technologies have reduced caring costs, providing a potential pathway for developing healthcare services for AD patients. For instance, if a virtual reality (VR) system can provide emotion-adaptive content, the time that AD patients spend interacting with VR content is expected to be extended, allowing caregivers to focus on other tasks. As the first step towards this goal, in this study, we develop a classification model that detects AD patients' emotions (e.g., happy, peaceful, or bored). We first collected electroencephalography (EEG) data from 30 Korean female AD patients who watched emotion-evoking videos at a medical rehabilitation center. We applied conventional machine learning algorithms, such as a multilayer perceptron (MLP) and support vector machine, along with deep learning models of recurrent neural network (RNN) architectures. The best performance was obtained from MLP, which achieved an average accuracy of 70.97%; the RNN model's accuracy reached only 48.18%. Our study results open a new stream of research in the field of EEG-based emotion detection for patients with neurological disorders.

Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia

Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The K nearest neighbor classifier is applied to the extracted features to classify four (happy, relaxed, angry, and sad) emotions. Our experimental results show that among individual modalities, PPG-based features gives the highest accuracy of 78.57% as compared to EEG- and GSR-based features. The fusion of EEG, GSR, and PPG features further improved the classification accuracy to 79.76% (for four emotions) when interacting with tactile enhanced multimedia.