The document discusses developing a brain-computer interface (BCI) using electroencephalography (EEG) to measure sustained attention and modulate performance in a complex monitoring task. It involves: 1) Measuring a user's sustained attention using EEG signals during a long-duration monitoring simulation task. 2) Developing a novel "dynamic adaptive thresholds" approach to classify sustained attention levels in real-time. 3) Integrating this classification with a feedback controller to provide countermeasures that aim to enhance performance without disrupting the task. The goal is to create a first neuroadaptive enterprise system interface using a design science methodology. An experiment was conducted to evaluate the BCI's effects on sustained

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. © 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. © 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. © 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. © 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. © 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. © 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. © 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. The experiment

10. The experiment
MONITORINGINTERFACE
MONITORINGINTERFACE
MONITORING INTERFACE
DECISION

11. The experiment
CRITICAL
NORMALCOUNTERMEASURE

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. © 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. © 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. © 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. © 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. © 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. SUSTAINED ATTENTION IN A MONITORING TASK:
Towards a neuroadaptative
enterprise system interface

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