Presented by: Deepika Dasari Neuroergonomic Analysis of Air
Transcription
Presented by: Deepika Dasari Neuroergonomic Analysis of Air
Neuroergonomic Analysis of Air Traffic Control Performance Part III: Assessment of mental state by EEG in ATC task Guofa Shou1, Deepika Dasari1, Lei Ding1,2 1School of electrical and computer engineering, University of Oklahoma. 2Center of biomedical engineering, University of Oklahoma. Presented by: Deepika Dasari Oklahoma/Kansas Judgment and Decision Making Group Workshop- Oklahoma City, OK, May 4, 2013 Outline • Introduction • EEG component based index • Low fidelity task • EEG component identification • Sensitivity to time on task • Sensitivity to mental workload • High fidelity tasks • Summary • Acknowledgements Introduction Prolonged durations on monotonous task can lead to degraded task performance, this phenomena is referred to time-on-task effect [1] Time on task effect is related to an excessive feeling of tiredness and reduced alertness, which impairs both capability and willingness to perform a task [2,3] Workload variations also affect the task performance Brain activity is believed to be a sensitive measure of mental states (i.e., workload, engagement, effort, etc.) [3] [1] Åkerstedt, T., Kecklund, G., & Knuttson, A. (1991). Manifest sleepiness and the EEG spectral content during night work. Sleep, 14, 221–225. [2] Johns, M. W. (2000). A sleep physiologist’s view of the drowsy driver.Transportation Research Part F, 3, 241–249. doi: org/10.1016/S1369-8478(01)00008-0 [3] Craig, A., Tran, Y., Wijesuriya, N., & Boord, P. (2006). A controlled investigation into the psychological determinants of fatigue. Biological Psychology, 72, 78–87. doi: org/10.1016/j.biopsycho.2005.07.005 ATC tasks Air traffic controller (ATC) studies Low Fidelity (C-Team) High Fidelity (CRA & ATCARS) Motor R R Pre-Motor L L R Occipital ◊ Theta (4-7Hz) (4-7Hz),Alpha (8-12Hz),Beta (12-30 Hz) ◊ Theta/Alpha, Theta/Beta Theta/Beta, Beta/(Theta+Alpha) L Secondary Somotasensory • Different EEG indexes are calculated in terms frequency bands as a function of time Frontal • IC patterns can be divided into different groups based on spatialspectral patterns [4] Central Medial EEG component based Index Bilateral Tangential Medial [4] Shou, G., Ding, L., & Dasari, D. (2012). Probing neural activations from continuous EEG in a real-world task: Timefrequency independent component analysis. Journal of Neuroscience Methods. Left Right Low fidelity: C-Team task C-Team (i.e., Controller Teamwork Evaluation and Assessment Methodology) was developed for the training and practice of air traffic controllers [5] Participants: 10 Sessions: • one training session (0.5 hr) • two recording sessions (2 hrs) Performance data • number of clicks CTEAM Task ATC Selection Laboratory [5] Bailey, L.L., Broach, D.M., Thompson, R.C., Enos, R.J., 1999. Controller Teamwork Evaluation and Assessment Methodology: A Scenario Calibration Study. Federal Aviation Administration Office of Aviation Medicine, Washington, DC. EEG ICs of interest Group level ICA, is performed after combining EEG data from all sessions based on brain’s microstates [1] Two ICs with evident spatial and spectral pattern were investigated for workload and time-on-task effect. [1] Lehmann D (1990) Brain electric microstates and cognition: the atoms of thought. In: E.R. John (ed): Machinery of the Mind. Birkhäuser, Boston. pp. 209-224. Sensitivity to time on task The Beta/(Theta+Alpha) index is sensitive to engagement[7] Linear regression is performed on spectral power data from engagement index to investigate time-on-task effect 12 (out of 20) sessions were identified with significant negative slope [7] Freeman F.G., Mikulka P.J., Prinzel L.J. & Scerbo M.W., “Evaluation of an adaptive automation system using three EEG indices with a visual tracking task,” in Biological Psychology, 50, pp. 61–76, 1999 Sensitivity to workload Correlation Correlation analysis between spectral power from different EEG Indexes to number of operations (i.e. clicks) was performed to investigate sensitivity to workload. Mental workload was sensitive to Theta/Alpha EEG Index, showing significant number of detection in binomial test Theta No of positive correlations 11 (out of 20 ) Theta/Alpha Theta/Beta 15 (out of 20 ) 11 (out of 20 ) High fidelity: CRA task CRA : Conflict Resolution Advisory Time-on- task: a negative slope for linear Mental workload: two EEG indexes regression for engagement for two Theta/Alpha, and Theta/Beta were occipital related IC. sensitive to workload. Workload Engagement Index Correlation P Coefficient Theta 0.06 0.69 Theta/Alpha 0.34 1.94E-02 Theta/Beta 0.399 5.9E-03 High fidelity: ATCARS task Engagement Index ATCAR: Air Traffic Control Advanced Research Simulator Similar IC patterns were identified in the high fidelity ATCAR task Engagement index is sensitive to the time-on-task effect Further data collection and analysis are in progress Participant 1 Participant 2 Participant 3 Summary ICA analysis was used to decompose EEG data into different brain related ICs Similar spatial patterns were identified across task levels Component based EEG Indexes were sensitive to time-on-task and mental workload in low-fidelity and high-fidelity tasks Further investigations to assess the specificity of these EEG indices to mental effort, mental workload and mental fatigue will be conducted These results can augment in developing real-time systems to monitor mental state of ATCs Acknowledgements Research Support: NSF CAREER ECCS-0955260, DOT-FAA 10-G-008, and OCAST HR09-125S. Thanks to Dr. Bailey, Dr. Millan and their team at FAA-CAMI for data collection efforts. Contact Information: Computational Neuroimaging & Neuroengineering Lab (CNNL) Address: University of Oklahoma 3100 Monitor Ave. Suite 280 Phone: (405) 325-3774 Website: http://www.ou.edu/ouneuro/ E-mail: [email protected], [email protected], [email protected]