Analysis of multimodal physiological signals within and between individuals to predict psychological challenge vs. threat
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The notion that discrete emotion categories can be reliably identified by specific behavioural patterns and/or physiological features has been seriously called into doubt by marked variability within instances of the same category as well as similarities across categories.
“The notion that discrete emotion categories can be reliably identified by specific behavioural patterns and/or physiological features has been seriously called into doubt by the marked variability within instances of the same category as well as similarities across categories.”
From A new science of emotion: implications for functional neurological disorder (2022) · cites this paper as [61]
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A new science of emotion: implications for functional neurological disorder
Jungilligens, J., Paredes-Echeverri, S., Popkirov, S., Barrett, L. F., & Perez, D. L. (2022) · Brain · doi:10.1093/brain/awac204 · Cited by 164 · see citing works
Self-citationContext-aware experience sampling reveals the scale of variation in affective experience
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Self-citationThe Theory of Constructed Emotion: More Than a Feeling
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“…2020; Jack et al., 2016; Le Mau et al., 2021; Srinivasan and Martinez, 2021), autonomic nervous system physiology (e.g., Hoemann, Khan et al., 2020; Khalaf et al., 2020; McVeigh et al., 2023; Kragel and LaBar, 2013; Siegel et al., 2018; Stemmler et al., 2007; Stephens et al., 2010), and brain…”
Self-citationPilot Stress Detection Through Physiological Signals Using a Transformer-Based Deep Learning Model
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“Khalaf et al. [29] identified the ECG, EMG, EDA, BP, and impedance cardiogram (ICG) features that are most associated with within-individual physiological changes under different tasks.”
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Machine Learning for Stress Detection from Electrodermal Activity: A Scoping ReviewPreprint
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“[12] [13] [14] [15] [16] [17] [18] [19] [13] [20] [21] [22] [23] [24] [12] [15] [16] [25] [26] [27] [28] [27] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [24] [35] [36] [40] [37] [41] [42] [43] [44] [45] [46] [47] [36] [48] [49] [50] [51] [52] [53] [54] [48] [55] [50] [15] [46] [49] [20] [38] [56] [48] [57]…”
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Measuring Arousal: Promises and Pitfalls
Reid, T., Nielson, C., & Wormwood, J. B. (2024) · Affective Science · doi:10.1007/s42761-024-00288-4 · Cited by 16 · see citing works
“Relatedly, covariance among ANS measures during motivated performance tasks can also change within people over time (e.g., Khalaf et al., 2020).”
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Prajapati, V., Guha, R., & Routray, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254335 · Cited by 15 · see citing works
Estimating Systemic Cognitive States from a Mixture of Physiological and Brain Signals
Scheutz, M. J., Aeron, S., Aygun, A., de Ruiter, J. P., Fantini, S., Fernandez, C. M., Haga, Z., Nguyen, T. D., & Lyu, B. (2023) · Topics in Cognitive Science · doi:10.1111/tops.12669 · Cited by 13 · see citing works
“In [64], authors recorded various physiological signals such as ECG, continuous blood pressure, respiration, impedance cardiogram, and facial electromyography (EMG) to assess the participant’s challenge and threat states who completed three mental arithmetic tasks.”
BackgroundKleckner, I. R., Wormwood, J. B., Jones, R. M., Culakova, E., Barrett, L. F., Lord, C., Quigley, K. S., Goodwin, M. S., Quigley, K., & Goodwin, M. (2021) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2023.112280 · Cited by 12 · see citing works
Self-citationContext-aware experience sampling reveals the scale of variation in affective experiencePreprint
Hoemann, K., Khan, Z., Feldman, M. J., Nielson, C., Devlin, M., Dy, J., Barrett, L. F., Wormwood, J. B., & Quigley, K. S. (2020) · doi:10.31234/osf.io/cvjb8 · Cited by 14 · see citing works
Self-citationAffect and Social Judgment: The Roles of Physiological Reactivity and Interoceptive Sensitivity
Feldman, M. J., Siegel, E. H., Barrett, L. F., Quigley, K. S., & Wormwood, J. B. (2022) · Affective Science · doi:10.1007/s42761-022-00114-9 · Cited by 11 · see citing works
“Findings from some of these additional questionnaires and tasks have been described elsewhere (see Khalaf et al., 2020; Wormwood, Khan, Siegel., 2019).”
BackgroundSelf-citationJiang, H., & Yin, X. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.918274 · Cited by 7 · see citing works
“By integrating the inter-modal similarity and intra-modal similarity into the graph regularization term (Khalaf et al., 2020), the relationship between multimodal data and the relationship between the data within each mode can be better represented.”
BackgroundOn Using Physiological Sensors and AI to Monitor Emotions in a Bug-Hunting Game
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“[22] and the placement of electrodes for ECG-EDA-ICG recordings using the VU-AMS device [43].”
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“In addition, the inclusion of physiological time series data is a promising avenue for increasing predictive power (for an example, see (Khalaf et al., 2020)).”
Unsupervised learning for physiological signals in real-life emotion recognition using wearables
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“Yet the promise of wearables remains constrained by a fundamental challenge: physiological responses are nonlinear (Baxt 1994), context-dependent (Khalaf et al 2020), and vary widely across individuals (Chen et al 2017a, Can et al 2023).”
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- Authors
- Khalaf, A., Nabian, M., Fan, M., Yin, Y., Wormwood, J., Siegel, E., Quigley, K. S., Barrett, L. F., Akcakaya, M., Chou, C.-A., & Ostadabbas, S.
- Affiliations
- University of Pittsburgh (US)
- Northeastern University (US)
- University of New Hampshire (US)
- University of California, San Francisco (US)
- Corresponding author
- Ostadabbas, Sarah
- ORCID
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 140
- Pages
- 112890
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2969221148
- Cited by
- 25
- References
- 53
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 7 · 2021: 2 · 2020: 3
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Social Sciences › Psychology › Developmental and Educational Psychology › Sport Psychology and Performance
- Keywords
- individual differences, multimodal physiological signals, task difficulty, mental arithmetic, feature selection, support vector machine classification
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