Stress is defined as a state that arises when external or internal demands exceed an individual's coping resources. (Hobfoll et al., 1993).

In realistic contexts, it is common to encounter stressful factors such as high task demand, lack of control, frustration and time pressure. Such stressors negatively influence performance, altering the cognitive processes at the base of decision-making, attention and memory.

A non-invasive measure of the stress level is the electrodermal activity (EDA). When an individual is under stress, the activity of the sweat gland increases, which is reflected in an increase of skin conductance. Since sweat glands are also controlled by the Sympathetic Nervous System (SNS), skin conductance acts as an indicator of sympathetic activation in response to stress (Sequeira et al., 2009).

Another reliable measure of stress can be obtained using EEG signals. In particular, it has been found that in presence of stressors, alpha power decreases in the prefrontal cortex and beta power increases in the temporal and parietal sites (Al-Shargie et al., 2016). In different contexts, it has been proven that stressful conditions induce asymmetry in brain activations (Murat et al., 2009). The right brain hemisphere has been shown to be mainly involved in cortisol production, rather than the left hemisphere (Lewis et al., 2007).

BrainSigns developed and validated measures of stress for out-of-the-lab use, both by using the skin conductance level, and the EEG signal, with particular regard to the beta band activity in the right parietal brain area. Such algorithm has been validated within the Mindtooth project (Sciaraffa et al., 2021).

The stress index is employed in BrainSigns for user’s wellbeing assessment in the context of training, as well as to evaluate the user’s perception in interacting with different technologies. In the field of human factors, it is used in automotive (Sciaraffa et al., 2021) and aviation (Borghini et al, 2020) contexts.

Stress is one of the neurometrics available on the Mindtooth Neurometrics App, developed by BrainSigns.

REFERENCES

• Al-Shargie, M. Kiguchi, N. Badruddin, S. C. Dass, A. F. M. Hani, and T. B. Tang, “Mental stress assessment using simultaneous measurement of EEG and fNIRS,” Biomed. Opt. Express, vol. 7, no. 10, p. 3882, Oct. 2016.
• Borghini, Gianluca, et al. "A multimodal and signals fusion approach for assessing the impact of stressful events on Air Traffic Controllers." Scientific reports 10.1 (2020): 8600.
• Hobfoll, S. E., & Shirom, A. (1993). Stress and burnout in the workplace conservation of resources. In R. T. Golembiewski (Ed.), Handbook of organizational behavior (pp. 41-60). New York
• Lewis, N. Y. Weekes, and T. H. Wang, “The effect of a naturalistic stressor on frontal EEG asymmetry, stress, and health,” Biol. Psychol., vol. 75, no. 3, pp. 239–247, Jul. 2007.
• Murat, M. N. Taib, Z. M. Hanafiah, S. Lias, R. S. S. A. Kadir, and H. A. Rahman, “Initial investigation of brainwave synchronization after five sessions of horizontal rotation intervention using EEG,” Proc. 2009 5th Int. Colloq. Signal Process. Its Appl. CSPA 2009, pp. 350–354, 2009.
• Sciaraffa, Nicolina, et al. "Validation of a light EEG-based measure for real-time stress monitoring during realistic driving." Brain sciences 12.3 (2022): 304.
• Sequeira, P. Hot, L. Silvert, and S. Delplanque, “Electrical autonomic correlates of emotion,” Int. J. Psychophysiol., vol. 71, no. 1, pp. 50–56, Jan. 2009.