Abstract
Epidermal Activity (EDA) has proved difficult for use in predicting workload states in complex tasks. Furthermore, a reliance on a single phasic component of the EDA may mask the true effects of workload on EDA. We hypothesized that decomposing a single time series into multiple principal components would identify a variety of phasic components reflecting the effects of task workload. In a 30-min simulated flight, pilots flew in two workload conditions which varied by task complexity. Mixed-factor ANOVA was used to analyze workload (within-subjects) and age (between-group) effects on the phasic components. The largest phasic component showed a marginal effect of workload, where higher workload was associated with larger EDA. Likewise, the smallest phasic component showed a significant main effect of workload, where higher workload was associated with larger EDA. Additionally, there was a significant effect of age, such that older pilots demonstrated larger EDA as compared to younger pilots. Results show that it is advantageous to decompose EDA time series data into multiple components of phasic data when predicting workload. Further work should be done to increase our understanding of using principal components of EDA in predicting workload during complex tasks.
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References
Northcutt, M.: General aviation accident rate: how general aviation differs from commercial airline flight and how to correct the discrepancy. J. AIR L. COM. 78, 381 (2013)
Li, G., Baker, S.P., Qiang, Y., Grabowski, J.G., McCarthy, M.L.: Driving-while-intoxicated history as a risk marker for general aviation pilots. Acc. Anal. Prevent. 37(1), 179–184 (2005)
Shi, Y., Ruiz, N., Taib, R., Choi, E., Chen, F.: Galvanic skin response (GSR) as an index of cognitive load. In: Conference on Human Factors in Computing Systems - Proceedings, pp. 2651–2656 (2007). https://doi.org/10.1145/1240866.1241057
Ikehara, C.S., Crosby, M.E.: Assessing cognitive load with physiological sensors. In: Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 295 (2005). https://doi.org/10.1109/HICSS.2005.103
Bari, D.S., Yacoob Aldosky, H.Y., Martinsen, Ø.G.: Simultaneous measurement of electrodermal activity components correlated with age-related differences. J. Biol. Phys. 46(2), 177–188 (2020). https://doi.org/10.1007/S10867-020-09547-4/TABLES/4
Gavazzeni, J., Wiens, S., Fischer, H.: Age effects to negative arousal differ for self-report and electrodermal activity. Psychophysiol. 45(1), 148–151 (2008). https://doi.org/10.1111/J.1469-8986.2007.00596.X
Aupée, A.M.: Effect of Normal Aging on Emotional Processing and Impact of Emotion on Memory : Psychophysiological and Cognitive Findings. Thesis, Lund University (2006). http://lup.lub.lu.se/record/546007
Posada-Quintero, H.F., Chon, K.H.: Phasic component of electrodermal activity is more correlated to brain activity than tonic component. In: 2019 IEEE EMBS International Conference on Biomedical Health Informatics (BHI), pp. 1–4 (2019). https://doi.org/10.1109/BHI.2019.8834567
Groth, A., Ghil, M.: Monte Carlo Singular Spectrum Analysis (SSA) revisited: detecting oscillator clusters in multivariate datasets. J. Clim. 28, 7873–7893 (2015)
Broomhead, D.S., King, G.P.: Extracting qualitative dynamics from experimental data. Physica D 20, 217–236 (1986)
Hassani, H.: Singular spectrum analysis: methodology and comparison. J. Data Sci. 5, 239–257 (2007)
Aladağ, S., Ayşegül, G., Nazan, D., Hatice, Ö.: The Role of sports participation in hemispheric dominance: assessment by electrodermal activity signals. IU-JEEE 17(1), 3113–3119 (2017)
Delorme, A., Makeig, S.: EEGLAB: an open-source toolbox for analysis of single-trial EEG dynamics. J. Neurosci. Methods 134, 9–21 (2004)
Harmony, T.: The functional significance of delta oscillations in cognitive processing. Front. Integr. Neurosci. 7(December), 83 (2013). https://doi.org/10.3389/fnint.2013.00083
Vautard, R., Yiou, P., Ghil, M.: Singular-spectrum analysis: A toolkit for short, noisy chaotic signals. Phys. D 58(1–4), 95–126 (1992)
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Sian, S., Pejemsky, A.D., Van Benthem, K., Herdman, C.M. (2022). Beyond Skin-Deep Investigations of Epidermal Activity to Predict Mental Workload Using Multiple Phasic Components: Implications for Real-Time Analysis Using Affordable Wearables. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1581. Springer, Cham. https://doi.org/10.1007/978-3-031-06388-6_30
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