Real Time Research Methods

Abstract

For the FAA’s Next Generation Air Transportation System (NextGen), the ability to reliably measure the effects of automation changes or new task demands on controller/pilot workload and performance is critical. Much of the EEG research has focused on identifying specific cognitive states associated with levels of workload during post processing. However, there are potential benefits for researchers to having EEG results as a continuous measure that can be observed during simulation experiments. This demonstration is a workload gauge that processes EEG data in real time as the Air Traffic Controller (ATC) participant is performing basic cognitive and operational ATC tasks. The visualizations demonstrated are also available during post processing to enable investigation of the continuous relationships between ATC situational variables, controller performance, and EEG.

Get full access to this article

View all access options for this article.

References

Berka

Chris

Levendowski

Daniel J.

Lumicao

Michelle N.

Yau

Alan

Davis

Gene

Zivkovic

Vladimir T.

Olmstead

Richard E.

Tremoulet

Patrice D.

Craven

Patrick L.

(2007) EEG Correlates of Task Engagement and Mental Workload in Vigilance, Learning, and Memory Tasks. Aviation, Space, and Environmental Medicine 78(5), 231–244.

Brookings

J.B.

Wilson

G.F.

Swain

C.R.

(1996). Psychophysiological responses to changes in workload during simulated air traffic control. Biological Psychology, 42(3), 361–377.

Caywood

M.S.

Greenwald

H.S.

Roberts

D.M.

Colombe

J.B.

Weiland

M.Z.

(2011). EEG correlates of dynamic workload in enroute air traffic control. Poster presented at the annual meeting of the Society for Neuroscience, Washington, DC.

Delorme

Mullen

Kothe

Akalin Acar

Bigdely-Shamlo

Vankov

Makeig

(2011). EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing. Computational Intelligence and Neuroscience, Article ID 130714.

Dussault

Jouanin

J-C.

Philippe

Guezennec

(2005). EEG and ECG Changes During Simulator Operation Reflect Mental Workload and Vigilance. Aviation, Space, and Environmental Medicine, 76 (4), 2005.

Gevins

Smith

M.E.

Leong

McEvoy

Whitfield

Rush

(1998). Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Human Factors, 40(1), 79–91.

Grimes

Tan

D.S.

Hudson

S.E.

Shenoy

Rao

R. P.N.

(2008). Feasibility and Pragmatics of Classifying Working Memory Load with an Electroencephalograph. In Proceedings of the Twenty-sixth Annual SIGCHI Conference on Human Factors in Computing Systems, 835–844.

Lemm

Blankertz

Dickhaus

Muller

(2011) Introduction to machine learning for brain imaging, NeuroImage, 56, 387-399.

Majumdar

Ochieng

W.Y.

(2002). The factors affecting air traffic controller workload: a multivariate analysis based upon simulation modeling of controller workload. Paper presented at the 81st Annual Meeting of the Transportation Research Board, Washington, D.C.

10.

Sterman

. Application of quantitative EEG analysis to workload assesment in an advanced aircraft simulator. Proceedings of Human Factors Society; Seattle, WA; 1993; Santa Monica, CA: Human Factors Society; 1993.

11.

Wientjes

C.J.E.

ter Maat

Gaillard

A.W.K.

(1996). Measurement of transcutaneous Pco2 among air traffic controllers: differential responding to workload and stress, Biological Psychology, 43(3), 250–251.

12.

Wierwille

W.W.

Eggemeier

F.T.

(1993). Recommendations for mental workload measurement in a test and evaluation environment. Human Factors, 35(2), 263-281.

13.

Wilson

G. F.

(2002): An Analysis of Mental Workload in Pilots During Flight Using Multiple Psychophysiological Measures. The International Journal of Aviation Psychology, 12(1), 3-18.

14.

Wilson

G.F.

Russell

C.A.

(2003). Operator functional state classification using multiple psychophysiological features in an air traffic control task. Human Factors, 45(3), 381-389.