FEASIBILITY RECORDING SIMULTANEOUSLY ELECTRODERMAL ACTIVITY PARAMETERS: A REVIEW

2018 ◽  
Author(s):  
Dindar Bari ◽  
◽  
Haval Aldosky
Keyword(s):  
Electrodermal Activity

In Search of Somatic Precursors of Spontaneous Insight

2018 ◽  
Vol 32 (3) ◽  
pp. 97-105 ◽  
Author(s):  
Wangbing Shen ◽  
Yuan Yuan ◽  
Chaoying Tang ◽  
Chunhua Shi ◽  
Chang Liu ◽  
...  
Keyword(s):  
Problem Solving ◽  
Direct Evidence ◽  
Electrodermal Activity ◽  
Cardiovascular Responses ◽  
Solution Time ◽  
Problem Solution ◽  
Insight Problem ◽  
Creative Insight ◽  
Insight Problem Solving ◽  
Analytic Problem

Abstract. A considerable number of behavioral and neuroscientific studies on insight problem solving have revealed behavioral and neural correlates of the dynamic insight process; however, somatic correlates, particularly somatic precursors of creative insight, remain undetermined. To characterize the somatic precursor of spontaneous insight, 22 healthy volunteers were recruited to solve the compound remote associate (CRA) task in which a problem can be solved by either an insight or an analytic strategy. The participants’ peripheral nervous activities, particularly electrodermal and cardiovascular responses, were continuously monitored and separately measured. The results revealed a greater skin conductance magnitude for insight trials than for non-insight trials in the 4-s time span prior to problem solutions and two marginally significant correlations between pre-solution heart rate variability (HRV) and the solution time of insight trials. Our findings provide the first direct evidence that spontaneous insight in problem solving is a somatically peculiar process that is distinct from the stepwise process of analytic problem solving and can be represented by a special somatic precursor, which is a stronger pre-solution electrodermal activity and a correlation between problem solution time and certain HRV indicators such as the root mean square successive difference (RMSSD).

Maternal Perception of Infants’ Expressions of Emotion

2010 ◽  
Vol 24 (3) ◽  
pp. 173-185 ◽  
Author(s):  
Martin Krippl ◽  
Stephanie Ast-Scheitenberger ◽  
Ina Bovenschen ◽  
Gottfried Spangler
Keyword(s):  
Response Pattern ◽  
Electrodermal Activity ◽  
Startle Amplitude ◽  
Caregiving System ◽  
Parental Caregiving ◽  
Opponent Process ◽  
Opponent Process Theory ◽  
Valence And Arousal ◽  
Methodological Considerations

In light of Lang’s differentiation of the aversive and the approach system – and assumptions stemming from attachment theory – this study investigates the role of the approach or caregiving system for processing infant emotional stimuli by comparing IAPS pictures, infant pictures, and videos. IAPS pictures, infant pictures, and infant videos of positive, neutral, or negative content were presented to 69 mothers, accompanied by randomized startle probes. The assessment of emotional responses included subjective ratings of valence and arousal, corrugator activity, the startle amplitude, and electrodermal activity. In line with Lang’s original conception, the typical startle response pattern was found for IAPS pictures, whereas no startle modulation was observed for infant pictures. Moreover, the startle amplitudes during negative video scenes depicting crying infants were reduced. The results are discussed with respect to several theoretical and methodological considerations, including Lang’s theory, emotion regulation, opponent process theory, and the parental caregiving system.

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

Smiling won’t make you feel better: Response-focused emotion regulation strategies have little impact on cognitive, behavioural, physiological, and subjective outcomes

2020 ◽  
Author(s):  
Nancy Bahl ◽  
Allison Ouimet
Keyword(s):  
Emotion Regulation ◽  
Negative Emotion ◽  
Electrodermal Activity ◽  
Adaptive Strategy ◽  
Expressive Suppression ◽  
Memory Accuracy ◽  
Cognitive Behavioural ◽  
Regulation Strategies ◽  
The Common ◽  
Subjective Outcomes

Background and Objectives. Response-focused emotion regulation (RF-ER) strategies may alter people’s evoked emotions, influencing psychophysiology, memory accuracy, and affect. Researchers have found that participants engaging in expressive suppression (ES; a RF-ER strategy) experience increased sympathetic nervous system arousal, affect (i.e., higher subjective anxiety and negative emotion), and lowered memory accuracy. It is unclear, however, whether all RF-ER strategies exert maladaptive effects. Expressive dissonance (ED; displaying an expression opposite from how one feels) is a RF-ER strategy, and thus likely considered “maladaptive”. As outlined by the facial feedback hypothesis, however, smiling may increase positive emotion, suggesting it may be an adaptive strategy. We compared the effects of ED and ES to a control condition on psychophysiology, memory accuracy, and affect, to assess whether ED is an adaptive RF-ER strategy, relative to ES. Methods. We randomly assigned 144 female participants to engage in ED, ES, or to naturally observe, while viewing negative and arousing images. We recorded electrodermal activity and self-reported affect throughout the experiment and participants completed memory tasks. Results. There were no differences between groups across outcomes. Conclusion. Engaging in ES or ED may not lead to negative or positive impacts, shedding doubt on the common conclusion that specific strategies are categorically adaptive or maladaptive.

scholarly journals CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 52
Author(s):  
Tianyi Zhang ◽  
Abdallah El Ali ◽  
Chen Wang ◽  
Alan Hanjalic ◽  
Pablo Cesar
Keyword(s):  
Emotion Recognition ◽  
Electrodermal Activity ◽  
Short Form ◽  
Recognition Performance ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Recognition Algorithm ◽  
Fine Grained ◽  
Physiological Sensors ◽  
Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

scholarly journals Advancing Stress Detection Methodology with Deep Learning Techniques Targeting UX Evaluation in AAL Scenarios: Applying Embeddings for Categorical Variables

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1550
Author(s):  
Alexandros Liapis ◽  
Evanthia Faliagka ◽  
Christos P. Antonopoulos ◽  
Georgios Keramidas ◽  
Nikolaos Voros
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
User Experience ◽  
Electrodermal Activity ◽  
Binary Classification ◽  
Research Question ◽  
Classification Problem ◽  
Categorical Variables ◽  
Stress Detection ◽  
Software Failures

Physiological measurements have been widely used by researchers and practitioners in order to address the stress detection challenge. So far, various datasets for stress detection have been recorded and are available to the research community for testing and benchmarking. The majority of the stress-related available datasets have been recorded while users were exposed to intense stressors, such as songs, movie clips, major hardware/software failures, image datasets, and gaming scenarios. However, it remains an open research question if such datasets can be used for creating models that will effectively detect stress in different contexts. This paper investigates the performance of the publicly available physiological dataset named WESAD (wearable stress and affect detection) in the context of user experience (UX) evaluation. More specifically, electrodermal activity (EDA) and skin temperature (ST) signals from WESAD were used in order to train three traditional machine learning classifiers and a simple feed forward deep learning artificial neural network combining continues variables and entity embeddings. Regarding the binary classification problem (stress vs. no stress), high accuracy (up to 97.4%), for both training approaches (deep-learning, machine learning), was achieved. Regarding the stress detection effectiveness of the created models in another context, such as user experience (UX) evaluation, the results were quite impressive. More specifically, the deep-learning model achieved a rather high agreement when a user-annotated dataset was used for validation.

scholarly journals Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3956
Author(s):  
Youngsun Kong ◽  
Hugo F. Posada-Quintero ◽  
Ki H. Chon
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
Smartphone Application ◽  
Real Time Processing ◽  
Pain Stimulation ◽  
Significant Difference ◽  
Before And After ◽  
Electrical Pulses ◽  
Pain Detection ◽  
High Level

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.

Sign in / Sign up

Export Citation Format

Share Document