Visual and auditory reaction time for air traffic controllers using quantitative electroencephalograph (QEEG) data

Brain Informatics - Tập 1 - Trang 39-45 - 2014
Hussein A. Abbass1,2, Jiangjun Tang1, Mohamed Ellejmi3, Stephen Kirby3
1School of Engineering and Information Technology, University of New South Wales, Canberra, Australia
2Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
3Eurocontrol Experimental Centre, Brétigny-sur-Orge, France

Tóm tắt

The use of quantitative electroencephalograph in the analysis of air traffic controllers' performance can reveal with a high temporal resolution those mental responses associated with different task demands. To understand the relationship between visual and auditory correct responses, reaction time, and the corresponding brain areas and functions, air traffic controllers were given an integrated visual and auditory continuous reaction task. Strong correlations were found between correct responses to the visual target and the theta band in the frontal lobe, the total power in the medial of the parietal lobe and the theta-to-beta ratio in the left side of the occipital lobe. Incorrect visual responses triggered activations in additional bands including the alpha band in the medial of the frontal and parietal lobes, and the Sensorimotor Rhythm in the medial of the parietal lobe. Controllers' responses to visual cues were found to be more accurate but slower than their corresponding performance on auditory cues. These results suggest that controllers are more susceptible to overload when more visual cues are used in the air traffic control system, and more errors are pruned as more auditory cues are used. Therefore, workload studies should be carried out to assess the usefulness of additional cues and their interactions with the air traffic control environment.

Tài liệu tham khảo

Hopkin V (1971) Conflicting criteria in evaluating air traffic control systems. Ergonomics 14(5):557–564 Das S, Gandhi A, Mondal S (1997) Effect of premenstrual stress on audiovisual reaction time and audiogram. Indian J Physiol Pharmacol 41(1):67–70 Shenvi D, Balasubramanian P (1994) A comparative study of visual and auditory reaction times in males and females. Indian J Physiol Pharmacol 38:229–229 Klimesch W (1999) Eeg alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 29(2):169–195 Lubar JF, Swartwood MO, Swartwood JN, O’Donnell PH (1995) Evaluation of the effectiveness of eeg neurofeedback training for adhd in a clinical setting as measured by changes in tova scores, behavioral ratings, and wisc-r performance. Biofeedback Self-regul 20(1):83–99 Streitberg B, Röhmel J, Herrmann W, Kubicki S (1987) Comstat rule for vigilance classification based on spontaneous EEG activity. Neuropsychobiology 17(1–2):105–117 Ray WJ (1990) The electrocortical system. In: Tassinary LG (ed) Principles of psychophysiology: physical, social, and inferential elements. Cambridge University Press, Cambridge, pp 385–412 Torsvall L, åAkerstedt T (1987) Sleepiness on the job: continuously measured EEG changes in train drivers. Electroencephalogr Clin Neurophysiol 66(6):502–511 Alluisi EA, Coates GD, Morgan Jr, BB (1977) Effects of temporal stressors on vigilance and information processing. In: Vigilance. Springer, New York, pp 361–421 Beatty J, Greenberg A, Deibler WP, O’Hanlon JF (1974) Operant control of occipital theta rhythm affects performance in a radar monitoring task. Science 183(4127):871–873 OHanlon JF, Beatty J (1977) Concurrence of electroencephalographic and performance changes during a simulated radar watch and some implications for the arousal theory of vigilance. In: Vigilance. Springer, New York, pp 189–201 Gevins A, Smith ME, Leong H, McEvoy L, Whitfield S, Du R, Rush G (1998) Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Hum Factors 40(1):79–91 John E, Easton P (1995) Quantitative electrophysiological studies of mental tasks. Biol Psychol 40(1):101–113 Scerbo MW, Freeman FG, Mikulka PJ (2003) A brain-based system for adaptive automation. Theor Issues Ergon Sci 4(1–2):200–219 Sandford J, Turner A (1995) Manual for the integrated visual and auditory continuous performance test. Braintrain, Richmond Abbass H, Tang J, Amin R, Ellejmi M, Kirby S (2014) The computational air traffic control brain: computational red teaming and big data for real-time seamless brain-traffic integration. J Air Traffic Control 56(2):10–17 Abbass H, Tang J, Amin R, Ellejmi M, Kirby S (2014) Augmented cognition using real-time EEG-based adaptive strategies for air traffic control. In: international annual meeting of the human factors and ergonomic society, HFES, SAGE Woodworth RS, Schlosberg H (1954) Experimental psychology. Oxford and IBH Publishing, New Delhi Niruba R, Maruthy K (2011) Assessment of auditory and visual reaction time in type 2 diabetics-a case control study. Al Ameen J Med Sci 4(3):274 Shelton J, Kumar P (2010) Comparison between auditory and visual simple reaction times. Neurosci Med 1(1):30–32