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EEG and Stress

EEG and Stress. A possible use of EEG as a measure of psychological stress Yotam Sahar. Background. What is stress?. Perceived threat → fight, flight or freeze ( Selye , 1936) Two main conceptualizations: Cognitive Physiological. Cognitive stress.

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EEG and Stress

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  1. EEG and Stress A possible use of EEG as a measure of psychological stress YotamSahar

  2. Background

  3. What is stress? • Perceived threat → fight, flight or freeze (Selye, 1936) • Two main conceptualizations: • Cognitive • Physiological

  4. Cognitive stress • A gap between a subject's perception of a threatening situation's demands and his perception of the available resources to cope with it (Lazarus, 1966; Lazarus & Launier, 1978) • Perceived importance of success (Staal, 2004)

  5. Physiological stress (De Kloet, Joëls & Holsboer, 2005) 1 – APA axis: hypothalamic-pituitary-adrenal axis, a major part of the neuroendocrine system that controls reactions to stress

  6. Why measure stress? • Bio feedback (de Witt, 1980) • Training • Expensive responses (Smith et al., 2001) • Stress and motor task performance (Yerkes & Dodson, 1908) Stress

  7. Why not self report stress? • Self report biases during long tasks (Van de Ven, 2002) • Discrepancies between its results and the result of validated physiological measures (Bourne & Yaroush, 2003; Gopher & Braune, 1984)

  8. Physiological stress measurement • Well established methods (Gopher & Donchin, 1986; Yerkes & Dodson, 1908) • Galvanic Skin Response (GSR) • Pupil Diameter (PD)

  9. Physiological stress measurement • But - unsuitable for the simulator environment: • complex for integration (Fournier, Wilson & Swain,1999; Smith, Gevins, Brown, Karnik & Du, 2001; Van Orden, Limbert, Makeig & Jung, 2001) • No real-time indication (Benoit et al., 2009)

  10. Purpose • Finding additional physiologic measures of stress • Possible use in simulator environments • Low delays (allowing real-time indication) • Examining the possibility of measuring stress using EEG and specifically ERP

  11. Haak, et al. (2009) • Correlated EEG indication of eye blink frequency with experienced stress, observing higher frequency of eye blinks in stressful situations • Limitation: the use of mediating phenomena increases delays

  12. Kinney et al (1971) • Evidence of visual evoked responses (VER) as indication of stress in naval environments • The actual potentials that supposed to indicate stress are not specified

  13. Lewis, Weekes & Wang (2007) • A shift from greater left frontal activity during low stress to greater right frontal activity during high stress

  14. White, Kanazawa & Yee (2005) • Gender differences • During a stressor task: • Men and women - N100 suppression • Women only - disrupted P50

  15. Hypothesis • A shift will be recorded from greater left frontal activity during low stress to greater right frontal activity during high stress • During high stress: • Men and women will show N100 suppression • Women only will show disrupted P50* * Since the P50 is a very short-lasting potential, it may be difficult to correlate it with the stressful event.

  16. METHOD

  17. Participants • ~ 20 Students, half men half women • All right handed • Normal or corrected eye sight

  18. Experimental Task • 2D-manoeuvring target on a screen, and simultaneously performed a monitoring task using the joystick trigger, as a secondary task ….……………………………Joystick controlled crosshairs ………………………………………….Main target ………………….Secondary task (return inside allowed area by trigger clicking) …………Allowed area

  19. Experimental Task • Main target moves at 10 different speeds at different trials, creating stress differences between trials • Maximum speed exceeds expert’s best performance, preventing ceiling effect

  20. Measures • Performance • A continuous record (60 Hz resolution) of whether the crosshair is within the main target or not. Stressful events will be defined as points in time that the crosshair is not “on target” • Percent of time that crosshairs “on target”(90% of the grade) • Percent of time that the red rectangle inside “allowed area” (5% of the grade) • Hits rate – percent of hits of the total clicks (5% of the grade) • Stress • GSR (reference – as valid stress measure) • Pupil diameter (reference – as valid stress measure) • EEG + ERP (putative stress measures)

  21. Procedure • Inducing stress: • each participant’s financial reward will depend upon his/her performance (creating “importance of success according to Staal, 2004) • Present trial’s performance and current state of reward will be presented after each trial • Training: • pre-testing 3-5 participants (other than the 20 participants of the experiment) for defining learning-curve • Training phase for the 20 participants • Experimental phase: • 2 sessions of 10 trials (45 seconds per trial, one speed per trial), semi-randomly allocated (to prevent order-based alternative explanations)

  22. Expected results • Manipulation test: • Performance will deteriorate as speed increases • Mean GSR will increase as speed increases • Mean PD will increase as speed increases

  23. Expected results • Hypothesis test: EEG measures • Mean left frontal activity will decrease as speed increases • Mean right frontal activity will increase as speed increases ERP measures* • Average N100 suppression will be greater in high speed trials than in low speed trials • Average disrupted P50 will be greater for female participants relative to men participants only in high speed trials** * ERP measures will be based upon averaging for each trial all the measurements at events that crosshair was not “on target” ** Since the P50 is a very short-lasting potential, it may be difficult to correlate it with the stressful event

  24. References Benoit, A., Bonnaud, L., Caplier, A., Ngo, P., Lawson, L., Trevisan, D. G., Levacic, V., Mancas, C., & Chanel, G. (2009). Multimodal focus attention and stress detection and feedback in an augmented driver simulator. Personal and Ubiquitous Computing, 13(1), 33-41. Bourne, L. E., & Yaroush, R. A. (2003). Stress and cognition: A cognitive psychological perspective. Unpublished manuscript, NASA grant NAG2-1561.‏ de Witt, D. J. (1980). Cognitive and biofeedback training for stress reduction with university athletes. Journal of Sport Psychology.‏ Fournier, L. R., Wilson, G. F., & Swain, C. R. (1999). Electrophysiological, behavioral, and subjective indexes of workload when performing multiple tasks: manipulations of task difficulty and training. International Journal of Psychophysiology, 31(2), 129-145.‏ Gopher, D., & Braune, R. (1984). On the psychophysics of workload: Why bother with subjective measures?. Human Factors: The Journal of the Human Factors and Ergonomics Society, 26(5), 519-532. Gopher, D., & Donchin, E. (1986). Workload: An examination of the concept.‏ In Handbook of Perception and Performance: Cognitive Processes and Performance, K. Boff, L. Kaufman and J. Thomas (Eds.), pp. 41.1– 41.49 (New York: Wiley). Haak, M., Bos, S., Panic, S., & Rothkrantz, L. J. M. (2009). Detecting stress using eye blinks and brain activity from EEG signals. Proceeding of the 1st Driver Car Interaction and Interface (DCII 2008).‏ Kinney, J. A. S., McKay, C. L., Mensch, A., & Luria, S. M. (1971). The visual evoked cortical response as a measure of stress in naval environments: Methodology and analysis.(1) Slow flash rates.‏ Lazarus, R. S. (1966). Psychological stress and the coping process. New York: McGraw-Hill. Lazarus, R. S., & Launier, R. (1978). Stress-related transactions between persons and environment. In L. A. Pervin & M. Lewis (Eds.),Perspectives in interactional psychology (pp. 287–327). New York: Plenum. Lewis, R. S., Weekes, N. Y., & Wang, T. H. (2007). The effect of a naturalistic stressor on frontal EEG asymmetry, stress, and health. Biological psychology,75(3), 239-247.‏ Selye, H. (1936). A syndrome produced by diverse nocuous agents. Nature; Nature. Smith, M. E., Gevins, A., Brown, H., Karnik, A., & Du, R. (2001). Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction. Human Factors: The Journal of the Human Factors and Ergonomics Society, 43(3), 366-380.‏ Staal, M. A. (2004). Stress, cognition, and human performance: A literature review and conceptual framework. Ames Research Center.‏ Van de Ven, J.G.M. (2002). Getting a grip on mental workload. Defended 29-01-2002, KUN; prepared: KUN. Prom. prof.dr. G.P. van Galen, dr. A. de Haan. Nijmegen:NICI Van Orden, K. F., Limbert, W., Makeig, S., & Jung, T. P. (2001). Eye activity correlates of workload during a visuospatial memory task. Human Factors: The Journal of the Human Factors and Ergonomics Society, 43(1), 111-121.‏ White, P. M., Kanazawa, A., & Yee, C. M. (2005). Gender and suppression of mid‐latency ERP components during stress. Psychophysiology, 42(6), 720-725. Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit‐formation. Journal of comparative neurology and psychology,18(5), 459-482.

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