Electrodermal activity as a measure of cognitive load: a methodological approach

Buchwald, Mikołaj; Kupinski, Szymo; Bykowski, Adam; Marcinkowska, Joanna; Ratajczyk, Dawid; Jukiewicz, Marcin

doi:10.23919/spa.2019.8936745

Cited by 12 publications

(12 citation statements)

References 17 publications

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“…In the past, research groups have demonstrated effective utilization of other physiological signals along with EEG activity to analyse the effect of stress and cognition (Bianco et al, 2019;Chatterjee et al, 2019), and it was observed that these physiological signals were able to capture difference within different cognition states. With the use of single physiological signal acquisition, similar findings were obtained through accuracy was not much (Buchwald et al, 2019;Ali et al, 2018). In the present study, we have tried to observe if two commonly used;…”

Section: T a B L E 3 Performance Metrics Of All Classifiersmentioning

confidence: 62%

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Chirplet transform‐based machine‐learning approach towards classification of cognitive state change using galvanic skin response and photoplethysmography signals

et al. 2022

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Recent advances in the field of cognitive sciences have provided evidence to classify activities with different cognitive engagement levels. High cognitive load and sustained attention may predispose an individual to psychological and physical stress and may result in a lack of performance. Cognitive load monitoring of professionals in high precision fields has gathered significant attention in the current research scenario. The proposed work in continuity has tried to demonstrate the use of two non‐invasive physiological sensing modalities viz. galvanic skin response (GSR) and photoplethysmography (PPG) to elucidate effectiveness in picking up differences of two induced cognitive state categories based on machine learning and statistical methods. This paper demonstrates how GSR and PPG signals data can be effectively utilized to examine the shift in cognitive load. The present work proposes the use of general linear chirplet transform (GLCT) to evaluate the time‐frequency characteristic of GSR and PPG signals and utilize the statistical features in classification. Random Forest, Decision Tree, and k‐Nearest Neighbours demonstrated an accuracy of 92.13%, 88.0%, and 86.13% respectively on a dataset of 20 subjects against an optimized feature set, thus demonstrating the effectiveness of the proposed methodology for differentiating pre‐defined categories of cognitive load. The study shows the potential of GSR and PPG signals attributes and time‐frequency representation using GLCT to monitor cognitive load in real‐life conditions. The proposed work also indicates the possibility to extend the same to attention‐demanding fields such as aviation, medicine, and manufacturing for effective remediation of fatigue periods with increased cognitive demand, which, if sustained, may lead to cognitive decline in the long run.

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Section: T a B L E 3 Performance Metrics Of All Classifiersmentioning

confidence: 62%

“…EDA activity was observed by Buchwald et al (2019) during cognitive load state change analyses of 16 subjects while they were playing a videogame, performing faster breathing, and we are in a rest state. ANOVA test was performed on standardized conductance of EDA activity to signify the difference between various cognitive states.…”

Section: Introductionmentioning

confidence: 99%

Chirplet transform‐based machine‐learning approach towards classification of cognitive state change using galvanic skin response and photoplethysmography signals

et al. 2022

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“…Recently, however, researchers have found success using wrist-worn sensors to measure physiological signals to predict mental effort [ 19 ]. Further, researchers recently argued that EDA may be a strong physiological measure to use as an indicator of cognitive load [ 20 ]. In the next section, we examine how EDA has been used to measure cognitive load and conceptually related measures of mental effort and mental workload.…”

Section: Literature Reviewmentioning

confidence: 99%

Toward Mental Effort Measurement Using Electrodermal Activity Features

Romine

Schroeder

Banerjee

et al. 2022

Sensors

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The ability to monitor mental effort during a task using a wearable sensor may improve productivity for both work and study. The use of the electrodermal activity (EDA) signal for tracking mental effort is an emerging area of research. Through analysis of over 92 h of data collected with the Empatica E4 on a single participant across 91 different activities, we report on the efficacy of using EDA features getting at signal intensity, signal dispersion, and peak intensity for prediction of the participant’s self-reported mental effort. We implemented the logistic regression algorithm as an interpretable machine learning approach and found that features related to signal intensity and peak intensity were most useful for the prediction of whether the participant was in a self-reported high mental effort state; increased signal and peak intensity were indicative of high mental effort. When cross-validated by activity moderate predictive efficacy was achieved (AUC = 0.63, F1 = 0.63, precision = 0.64, recall = 0.63) which was significantly stronger than using the model bias alone. Predicting mental effort using physiological data is a complex problem, and our findings add to research from other contexts showing that EDA may be a promising physiological indicator to use for sensor-based self-monitoring of mental effort throughout the day. Integration of other physiological features related to heart rate, respiration, and circulation may be necessary to obtain more accurate predictions.

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“…Physiological and psychological engagement are linked to human metabolic processes. , The level of engagement can be reflected in several human physiological parameters such as skin temperature, electrodermal activity (EDA), and heart rate. − Physiological engagement, such as doing physical exercise, elevates the emission rate of CO 2 . , Performing cognitive tasks, a form of psychological engagement, has also been linked to higher CO 2 emission rates, although the evidence is limited . Yet, the influence of physiological and psychological engagement and their relative importance for NH 3 emissions from humans have not been well-documented.…”

Section: Introductionmentioning

confidence: 99%

Physiology or Psychology: What Drives Human Emissions of Carbon Dioxide and Ammonia?

Yang,

Bekö,

Wargocki

et al. 2024

Environ. Sci. Technol.

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Humans are the primary sources of CO 2 and NH 3 indoors. Their emission rates may be influenced by human physiological and psychological status. This study investigated the impact of physiological and psychological engagements on the human emissions of CO 2 and NH 3 . In a climate chamber, we measured CO 2 and NH 3 emissions from participants performing physical activities (walking and running at metabolic rates of 2.5 and 5 met, respectively) and psychological stimuli (meditation and cognitive tasks). Participants' physiological responses were recorded, including the skin temperature, electrodermal activity (EDA), and heart rate, and then analyzed for their relationship with CO 2 and NH 3 emissions. The results showed that physiological engagement considerably elevated per-person CO 2 emission rates from 19.6 (seated) to 46.9 (2.5 met) and 115.4 L/h (5 met) and NH 3 emission rates from 2.7 to 5.1 and 8.3 mg/h, respectively. CO 2 emissions reduced when participants stopped running, whereas NH 3 emissions continued to increase owing to their distinct emission mechanisms. Psychological engagement did not significantly alter participants' emissions of CO 2 and NH 3 . Regression analysis revealed that CO 2 emissions were predominantly correlated with heart rate, whereas NH 3 emissions were mainly associated with skin temperature and EDA. These findings contribute to a deeper understanding of human metabolic emissions of CO 2 and NH 3 .

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Electrodermal activity as a measure of cognitive load: a methodological approach

Cited by 12 publications

References 17 publications

Chirplet transform‐based machine‐learning approach towards classification of cognitive state change using galvanic skin response and photoplethysmography signals

Chirplet transform‐based machine‐learning approach towards classification of cognitive state change using galvanic skin response and photoplethysmography signals

Toward Mental Effort Measurement Using Electrodermal Activity Features

Physiology or Psychology: What Drives Human Emissions of Carbon Dioxide and Ammonia?

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