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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

EEG and Stress

A possible use of EEG as a measure of psychological stress


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

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


why not self report stress
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)
physiological stress measurement
Physiological stress measurement
  • Well established methods (Gopher & Donchin, 1986; Yerkes & Dodson, 1908)
    • Galvanic Skin Response (GSR)
    • Pupil Diameter (PD)
physiological stress measurement1
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)
  • 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
haak et al 2009
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
kinney et al 1971
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
lewis weekes wang 2007
Lewis, Weekes & Wang (2007)
  • A shift from greater left frontal activity during low stress to greater right frontal activity during high stress
white kanazawa yee 2005
White, Kanazawa & Yee (2005)
  • Gender differences
  • During a stressor task:
    • Men and women - N100 suppression
    • Women only - disrupted P50
  • 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
  • ~ 20 Students, half men half women
  • All right handed
  • Normal or corrected eye sight
experimental task
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

experimental task1
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
  • Performance
    • 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)
  • 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)
expected results
Expected results
  • Manipulation test:
    • Performance will deteriorate as speed increases
    • Mean GSR will increase as speed increases
    • Mean PD will increase as speed increases
expected results1
Expected results
  • Hypothesis test:
    • Mean left frontal activitywill decrease as speed increases
    • Mean right frontal activity will increase as speed increases
    • N100 suppression will take place in high speed trials
    • Disrupted P50 will take place for female participants only in high speed trials

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.