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Sustained attention in a monitoring task: Towards a neuroadaptative enterprise system interface

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Conference at NeuroIS 2018 in Vienna.
Authors : Demazure, Karran, Labonté-LeMoyne, Léger, Sénécal,
Fredette, Babin

Published in: Business
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Sustained attention in a monitoring task: Towards a neuroadaptative enterprise system interface

  1. 1. SUSTAINED ATTENTION IN A MONITORING TASK: Towards a neuroadaptative enterprise system interface Does modulation of the user ability to maintain a steady state of Sustained Attention (SA) in real-time while a complex monitoring task increases performance? Théophile Demazure Alexander Karran Élise Labonté-LeMoyne Pierre-Majorique Léger Sylvain Sénécal Marc Fredette Gilbert Babin HEC Montréal NeuroIS Retreat 2018, Vienna (Austria), June 19th - 21st
  2. 2. © Copyright Léger et al. 2018 Introduction ▪▪ Brain-Computer Interface (BCI): system which uses brain activity as an input (Wolpaw, 2002) ▪▪ Presentation: the development of a BCI using a Design Science Methodology ▪▪ Measure: EEG signal measuring Sustained Attention in a long duration task (time) ▪▪ Context of observation: Ecologicaly valid business task CONTRIBUTION: ▪▪ A novel approach to the Task Engage- ment Index and creation of a dynamic adaptive thresholds of SA user state ▪▪ First step of a potential BCI integration to enterprise system Wolpaw, J. R., and al. (2002). «Brain–computer interfaces for communication and control.» Clinical Neurophysiology 113(6): 767-791.
  3. 3. © Copyright Léger et al. 2018 IDENTIFY PROBLEM & MOTIVATE DEFINE OBJECTIVES OF A SOLUTION DESIGN & DEVELOPMENT DEMONSTRATION EVALUATION COMMUNICATION Objective- Centered Solution Design & Developmen t Centered Initiation Client/ Context Initiated Problem- Centered Initiation Process iteration Nominalprocess Possible entry points Methodological Approach DESIGN SCIENCE RESEARCH METHODOLOGY Research need : BCI opportunities to answer vigilance decrement. Figure : DSRM Process Model (Peffers and al., 2007) Peffers, K., and al. (2007). “A Design Science Research Methodology for Information Systems Research.” Journal of management information systems 24(3): 45-77.
  4. 4. © Copyright Léger et al. 2018 Requirement Analysis AUTOMATION HAS INCREASED PRODUCTIVITY BY REDUCING INFORMATION PROCESSING AND COGNITIVE LOAD, IT HAS DECREASED OPERATOR DECISION READINESS AND ON-TASK SAFETY, AND THAT ERRORS ARE OFTEN THE RESULT OF A DEC- REASE IN OPERATOR VIGILANCE AND SUSTAINED ATTENTION (PARASURAMAN, 1996 ; PARASURAMAN, 2010). Requirements : ▪▪ The artefact must represent a real information system monitoring task. ▪▪ The IS task and its duration must induce and promote a vigilance decrement in the user. ▪▪ The BCI component of the artefact should provide counter measures, to modulate the level of sustained attention leading to a performance enhancement of its user without obstruction of the IS task. Parasuraman, R., and al. (1996). “Effects of adaptive task allocation on monitor- ing of automated systems.” Human Factors 38(4): 665-679. Parasuraman, R. and D. H. Manzey (2010). “Complacency and bias in human use of automation: An attentional integration.” Human Factors 52(3): 381-410.
  5. 5. © Copyright Léger et al. 2018 Sustained Attention Sustained attention is the ability to main- tain an high degree of attention to detect stimuli during a long period of time (Mackworth, 1964; Olsen, 2006). Oken, B. S., and al. (2006). “Vigilance, alertness, or sustained attention: physiologi- cal basis and measurement.” Clin Neurophysiol 117(9): 1885-1901. Mackworth, J. F. (1964). “Performance decrement in vigilance, threshold, and high- speed perceptual motor tasks.” Canadian Journal of Psychology/Revue canadienne de psychologie 18(3): 209.
  6. 6. © Copyright Léger et al. 2018 Design and development BACK-END FRONT-END ERPsim on SAP HANA Feedback Controller API WEB APPLICATION INTERFACE Web Server OData API Connector Mensia NeuroRT Application Server Sensors DESIGN PRINCIPLES ▪▪ Reproduction of a real-life hands-on task ▪▪ Provide the user information about task and performance ▪▪ Provide real-time data from the system ▪▪ Design non-obstructive countermeasures ▪▪ Seamless integration to SAP architecture ▪▪ Real-time classification of user attention ▪▪ Synchronisation of physiological signals, simulation and user use of the interface data ▪▪ Computation of interaction decisions
  7. 7. © Copyright Léger et al. 2018 Sustained Attention Index Pope (1995) developped the ratio representing task engagement : β/(α+τ). Used for a vigilance task by Mikulka (2002) and Freeman (2004) to drive a closed biocybernetic loop with interesting results. Limit: the instability of the systems relying on it (Fairclough , 2016) Pope, A. T. B., Edward H.; Bartolonne, Debbie S. (1995). “Biocybernetic system evaluates indices of operator engagement in automated task.” Mikulka, P. J., and al. (2002). “Effects of a Biocybernetic System on Vigilance Performance.” Human Factors: The Journal of the Human Factors and Ergo- nomics Society 44(4): 654-664. Freeman, F. G., and al. (2004). “An evaluation of an adaptive automation system using a cognitive vigilance task.” Biol Psychol 67(3): 283-297 Ewing, K. C., and al. (2016). “Evaluation of an Adaptive Game that Uses EEG Measures Validated during the Design Process as In- puts to a Biocybernetic Loop.” Front Hum Neurosci 10: 223. Figure : Task Engagement Index (Pope, 1995) 𝛽 > (𝛼 + 𝜏) 𝛽 < (𝛼 + 𝜏) 𝛽/(𝛼 + 𝜏)
  8. 8. © Copyright Léger et al. 2018 Sustained Attention spectrum, a novel approach to the index DYNAMIC ADAPTIVE THRESHOLDS Benchmark sustained attention with a two task baseline inducing low and high degree of attention from the user. Every second, Thresholds are computed to follow the cumulative average of the index. COMPUTATIONAL CLASSIFIER Decisions of adaptiveness are based on value of the last five seconds of the index in the thresholds. The five seconds average and the user’s Dynamic Adaptive Thresholds is recalcu- lated every second. Figure : Dynamic Adaptive Thresholds T Index Thresholds System State Calculation System Adaptivity Decision High Max High Average Total Average Low Average Low Min
  9. 9. The experiment
  10. 10. The experiment MONITORINGINTERFACE MONITORINGINTERFACE MONITORING INTERFACE DECISION
  11. 11. The experiment CRITICAL NORMALCOUNTERMEASURE
  12. 12. Experimental Task CHARACTERISTICS ▪▪ 90 minutes long ▪▪ 2 types of task: Decision/Monitoring ▪▪ 2 types of event: New Stock/Sales ▪▪ Events occur every 4 minutes and 30 seconds
  13. 13. © Copyright Léger et al. 2018 Evaluation COUNTERMEASURES AND SUSTAINED ATTENTION Condition Subject Control 8 Continuous Countermeasures 8 Event-Related Counter Measures 8 Age Mean 26.73 Maximum 43 Minimum 18 Gender Subject F 11 M 13
  14. 14. © Copyright Léger et al. 2018 Evaluation SUSTAINED ATTENTION AND PERFORMANCE ▪▪ The Continuous Countermeasures Condition performed better than the other condition ▪▪ Each condition was able to assess their perceived performance correctly ▪▪ The Continuous Countermeasures Condition perceived workload is lower than the other condition
  15. 15. © Copyright Léger et al. 2018 Evaluation SUSTAINED ATTENTION AND PERFORMANCE 0 1 2 3 4 5 6 7 8 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 SIMSTART RESTOCK1 SALES1 SALES2 SALES3 SALES4 RESTOCK2 SALES5 SALES6 SALES7 SALES8 RESTOCK3 SALES9 SALES10 SALES11 SALES12 RESTOCK4 SALES13 SALES14 SALES15 NoCM - APM Mean by 1 Min Blocks 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 SIMSTART RESTOCK1 SALES1 SALES2 SALES3 SALES4 RESTOCK2 SALES5 SALES6 SALES7 SALES8 RESTOCK3 SALES9 SALES10 SALES11 SALES12 RESTOCK4 SALES13 SALES14 SALES15 CCM + NoCM - APM Mean by 1 Min Blocks 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 SIMSTART RESTOCK1 SALES1 SALES2 SALES3 SALES4 RESTOCK2 SALES5 SALES6 SALES7 SALES8 RESTOCK3 SALES9 SALES10 SALES11 SALES12 RESTOCK4 SALES13 SALES14 SALES15 CCM + ECM + NoCM - APM Mean by 1 Min Blocks
  16. 16. © Copyright Léger et al. 2018 Discussion CONTRIBUTION: ▪▪ A novel approach of sustained attention ▪▪ A first step of a embeddable BCI NEXT STEPS: ▪▪ Moving to more adaptive interaction ▪▪ Application of machine-learning to classify Sustained Attention ▪▪ Cross validation with different inputs (FNIRS) ▪▪ More granular real-time classification
  17. 17. © Copyright Léger et al. 2018 Conclusion LIMITS: ▪▪ Assessment of the user knowledge spe- cific to the task ▪▪ Usage of a transactional database ▪▪ Intrusiveness of the sensors SPECIFIC CHALLENGES TO DESIGN SCIENCE APPLIED TO BCI : ▪▪ Cost of experimental evaluation ▪▪ Simplify task
  18. 18. SUSTAINED ATTENTION IN A MONITORING TASK: Towards a neuroadaptative enterprise system interface
  19. 19. © Copyright Léger et al. 2018 DevelopmentDesignObjectives Definition Demonstrationand Evaluation REQUIREMENTS DESIGN PRINCIPLES FUNCTION/ MODULES DEVELOPMENT UNIT/ FUNCTIONAL TESTING INTEGRATION TESTING SYSTEM TESTING ACCEPTANCE USABILITY/UI TESTING GLOBAL ITERATION EVALUATION Development Unit/Functional Testing IntegrationTesting SystemTesting Acceptance & Usability/UI Testing Global Iteration Evaluation Interface • Tutorial • InterfaceDesign • Decisional Interface • Automaticdata refresh ScenariosTesting Behavior Analysis PerformanceAnalysis Data Consistency Interface/SAP Integration Interface/Controller Integration ProductionTesting ScenarioTesting: • Inputs : ArtificialEEG • Outputs: Interface Behavior, System Performance • Inputs : Scripted Interactions • Outputs: HANA data, Logs ExternalTesting Mistakes Spelling Bugs Interaction Observations Demonstrationsetup ResearchProtocol Evaluation: • Field Study • ControlledExperiment • Dynamic Analysis Feedback Controller • Signals Classification • Decision Computation • Logs • Integrationand APIs Testdata inputs Outputanalysis PerformanceAnalysis Stability Analysis Controller/SAP Integration Controller/EEG Pipeline Integration IterationPipeline

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