Galvanic Skin Response Features Research Articles

Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm.

This research aims to establish a practical stress detection framework by integrating physiological indicators and deep learning techniques. Utilizing a virtual reality (VR) interview paradigm mirroring real-world scenarios, our focus is on classifying stress states through accessible single-channel electroencephalogram (EEG) and galvanic skin response (GSR) data. Thirty participants underwent stress-inducing VR interviews, with biosignals recorded for deep learning models. Five convolutional neural network (CNN) architectures and one Vision Transformer model, including a multiple-column structure combining EEG and GSR features, showed heightened predictive capabilities and an enhanced area under the receiver operating characteristic curve (AUROC) in stress prediction compared to single-column models. Our experimental protocol effectively elicited stress responses, observed through fluctuations in stress visual analogue scale (VAS), EEG, and GSR metrics. In the single-column architecture, ResNet-152 excelled with a GSR AUROC of 0.944 (±0.027), while the Vision Transformer performed well in EEG, achieving peak AUROC values of 0.886 (±0.069) respectively. Notably, the multiple-column structure, based on ResNet-50, achieved the highest AUROC value of 0.954 (±0.018) in stress classification. Through VR-based simulated interviews, our study induced social stress responses, leading to significant modifications in GSR and EEG measurements. Deep learning models precisely classified stress levels, with the multiple-column strategy demonstrating superiority. Additionally, discreetly placing single-channel EEG measurements behind the ear enhances the convenience and accuracy of stress detection in everyday situations.

Multimodal and Multidomain Feature Fusion for Emotion Classification Based on Electrocardiogram and Galvanic Skin Response Signals

Emotion classification using physiological signals is a promising approach that is likely to become the most prevalent method. Bio-signals such as those derived from Electrocardiograms (ECGs) and the Galvanic Skin Response (GSR) are more reliable than facial and voice recognition signals because they are not influenced by the participant’s subjective perception. However, the precision of emotion classification with ECG and GSR signals is not satisfactory, and new methods need to be developed to improve it. In addition, the fusion of the time and frequency features of ECG and GSR signals should be explored to increase classification accuracy. Therefore, we propose a novel technique for emotion classification that exploits the early fusion of ECG and GSR features extracted from data in the AMIGOS database. To validate the performance of the model, we used various machine learning classifiers, such as Support Vector Machine (SVM), Decision Tree, Random Forest (RF), and K-Nearest Neighbor (KNN) classifiers. The KNN classifier gives the highest accuracy for Valence and Arousal, with 69% and 70% for ECG and 96% and 94% for GSR, respectively. The mutual information technique of feature selection and KNN for classification outperformed the performance of other classifiers. Interestingly, the classification accuracy for the GSR was higher than for the ECG, indicating that the GSR is the preferred modality for emotion detection. Moreover, the fusion of features significantly enhances the accuracy of classification in comparison to the ECG. Overall, our findings demonstrate that the proposed model based on the multiple modalities is suitable for classifying emotions.

Monitoring Inattention in Construction Workers Caused by Physical Fatigue Using Electrocardiograph (ECG) and Galvanic Skin Response (GSR) Sensors.

Physical fatigue is frequent for heavy manual laborers like construction workers, but it causes distraction and may lead to safety incidents. The purpose of this study is to develop predictive models for monitoring construction workers' inattention caused by physical fatigue utilizing electrocardiograph (ECG) and galvanic skin response (GSR) sensors. Thirty participants were invited to complete an attention-demanding task under non-fatigued and physically fatigued conditions. Supervised learning algorithms were utilized to develop models predicting their attentional states, with heart rate variability (HRV) features derived from ECG signals and skin electric activity features derived from GSR signals as data inputs. The results demonstrate that using HRV features alone could obtain a prediction accuracy of 88.33%, and using GSR features alone could achieve an accuracy of 76.67%, both through the KNN algorithm. The accuracy increased to 96.67% through the SVM algorithm when combining HRV and GSR features. The findings indicate that ECG sensors used alone or in combination with GSR sensors can be applied to monitor construction workers' inattention on job sites. The findings would provide an approach for detecting distracted workers at job sites. Additionally, it might reveal the relationships between workers' physiological features and attention.

Galvanic Skin Response Features in Psychiatry and Mental Disorders: A Narrative Review.

This narrative review is aimed at presenting the galvanic skin response (GSR) Biofeedback method and possibilities for its application in persons with mental disorders as a modern form of neurorehabilitation. In the treatment of mental disorders of various backgrounds and courses, attention is focused on methods that would combine pharmacological treatment with therapies improving functioning. Currently, the focus is on neuronal mechanisms which, being physiological markers, offer opportunities for correction of existing deficits. One such indicator is electrodermal activity (EDA), providing information about emotions, cognitive processes, and behavior, and thus, about the function of various brain regions. Measurement of the galvanic skin response (GSR), both skin conductance level (SCL) and skin conductance responses (SCR), is used in diagnostics and treatment of mental disorders, and the training method itself, based on GSR Biofeedback, allows for modulation of the emotional state depending on needs occurring. Summary: It is relatively probable that neurorehabilitation based on GSR-BF is a method worth noticing, which—in the future—can represent an interesting area of rehabilitation supplementing a comprehensive treatment for people with mental disorders.

A machine learning model for emotion recognition from physiological signals

Emotions are affective states related to physiological responses. This study proposes a model for recognition of three emotions: amusement, sadness, and neutral from physiological signals with the purpose of developing a reliable methodology for emotion recognition using wearable devices. Target emotions were elicited in 37 volunteers using video clips while two biosignals were recorded: photoplethysmography, which provides information about heart rate, and galvanic skin response. These signals were analyzed in frequency and time domains to obtain a set of features. Several feature selection techniques and classifiers were evaluated. The best model was obtained with random forest recursive feature elimination, for feature selection, and a support vector machine for classification. The results show that it is possible to detect amusement, sadness, and neutral emotions using only galvanic skin response features. The system was able to recognize the three target emotions with accuracy up to 100% when evaluated on the test data set.

A Self-Paced Relaxation Response Detection System Based on Galvanic Skin Response Analysis

Relaxation helps to reduce physical, mental, and emotional pressure. Relaxation techniques generally enable a person to obtain calmness and well-being by reducing stress, anxiety, or anger. When a person becomes calm the body reacts physiologically, producing the so-called Relaxation Response (RResp) which affects the organism in a positive manner, no matter if it is during a state of relaxation or in the middle of a stressful period. The goal of this paper is to design a system capable of identifying automatically the RResps of a subject by analyzing a single physiological signal, the galvanic skin response (GSR). To do so, a team composed of psychologists, neurologists, and engineers designed two experiments for inducing RResps in the participants while their GSR signals were being collected. The team analyzed the data and identified three different levels of RResp that can be quantified using only two easily calculated GSR features. Moreover, the use of the surface produced by GSR and its linear approximation is totally novel. Finally, the data were classified using decision tree strategies for each of the experiments and, after seeing that the obtained trees were similar, the team synthesized them in a single classification system. The F1 values obtained by the generalized classifier scored between 0.966 and 1.000 for the data collected in both experiments.

SVM-based feature selection methods for emotion recognition from multimodal data

Multimodal emotion recognition is an emerging field within affective computing that, by simultaneously using different physiological signals, looks for evaluating an emotional state. Physiological signals such as electroencephalogram (EEG), temperature and electrocardiogram (ECG), to name a few, have been used to assess emotions like happiness, sadness or anger, or to assess levels of arousal or valence. Research efforts in this field so far have mainly focused on building pattern recognition systems with an emphasis on feature extraction and classifier design. A different set of features is extracted over each type of physiological signal, and then all these sets of features are combined, and used to feed a particular classifier. An important stage of a pattern recognition system that has received less attention within this literature is the feature selection stage. Feature selection is particularly useful for uncovering the discriminant abilities of particular physiological signals. The main objective of this paper is to study the discriminant power of different features associated to several physiological signals used for multimodal emotion recognition. To this end, we apply recursive feature elimination and margin-maximizing feature elimination over two well known multimodal databases, namely, DEAP and MAHNOB-HCI. Results show that EEG-related features show the highest discrimination ability. For the arousal index, EEG features are accompanied by Galvanic skin response features in achieving the highest discrimination power, whereas for the valence index, EEG features are accompanied by the heart rate features in achieving the highest discrimination power.

Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interface

This article presents a framework of adaptive, measurable decision making for Multiple Attribute Decision Making (MADM) by varying decision factors in their types, numbers, and values. Under this framework, decision making is measured using physiological sensors such as Galvanic Skin Response (GSR) and eye-tracking while users are subjected to varying decision quality and difficulty levels. Following this quantifiable decision making, users are allowed to refine several decision factors in order to make decisions of high quality and with low difficulty levels. A case study of driving route selection is used to set up an experiment to test our hypotheses. In this study, GSR features exhibit the best performance in indexing decision quality. These results can be used to guide the design of intelligent user interfaces for decision-related applications in HCI that can adapt to user behavior and decision-making performance.

Emotional State Classification in Patient–Robot Interaction Using Wavelet Analysis and Statistics-Based Feature Selection

Due to a major shortage of nurses in the U.S., future healthcare service robots are expected to be used in tasks involving direct interaction with patients. Consequently, there is a need to design nursing robots with the capability to detect and respond to patient emotional states and to facilitate positive experiences in healthcare. The objective of this study was to develop a new computational algorithm for accurate patient emotional state classification in interaction with nursing robots during medical service. A simulated medicine delivery experiment was conducted at two nursing homes using a robot with different human-like features. Physiological signals, including heart rate (HR) and galvanic skin response (GSR), as well as subjective ratings of valence (happy-unhappy) and arousal (excited-bored) were collected on elderly residents. A three-stage emotional state classification algorithm was applied to these data, including: (1) physiological feature extraction; (2) statistical-based feature selection; and (3) a machine-learning model of emotional states. A pre-processed HR signal was used. GSR signals were nonstationary and noisy and were further processed using wavelet analysis. A set of wavelet coefficients, representing GSR features, was used as a basis for current emotional state classification. Arousal and valence were significantly explained by statistical features of the HR signal and GSR wavelet features. Wavelet-based de-noising of GSR signals led to an increase in the percentage of correct classifications of emotional states and clearer relationships among the physiological response and arousal and valence. The new algorithm may serve as an effective method for future service robot real-time detection of patient emotional states and behavior adaptation to promote positive healthcare experiences.