title: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks.
abstract: BACKGROUND
  The ability to continuously and unobtrusively monitor levels of task engagement and mental workload in an operational environment could be useful in identifying more accurate and efficient methods for humans to interact with technology. This information could also be used to optimize the design of safer, more efficient work environments that increase motivation and productivity.
  
  
  METHODS
  The present study explored the feasibility of monitoring electroencephalo-graphic (EEG) indices of engagement and workload acquired unobtrusively and quantified during performance of cognitive tests. EEG was acquired from 80 healthy participants with a wireless sensor headset (F3-F4,C3-C4,Cz-POz,F3-Cz,Fz-C3,Fz-POz) during tasks including: multi-level forward/backward-digit-span, grid-recall, trails, mental-addition, 20-min 3-Choice Vigilance, and image-learning and memory tests. EEG metrics for engagement and workload were calculated for each 1 -s of EEG.
  
  
  RESULTS
  Across participants, engagement but not workload decreased over the 20-min vigilance test. Engagement and workload were significantly increased during the encoding period of verbal and image-learning and memory tests when compared with the recognition/ recall period. Workload but not engagement increased linearly as level of difficulty increased in forward and backward-digit-span, grid-recall, and mental-addition tests. EEG measures correlated with both subjective and objective performance metrics.
  
  
  CONCLUSIONS
  These data in combination with previous studies suggest that EEG engagement reflects information-gathering, visual processing, and allocation of attention. EEG workload increases with increasing working memory load and during problem solving, integration of information, analytical reasoning, and may be more reflective of executive functions. Inspection of EEG on a second-by-second timescale revealed associations between workload and engagement levels when aligned with specific task events providing preliminary evidence that second-by-second classifications reflect parameters of task performance.
authors.affiliation: Advanced Brain Monitoring, Inc., 2850 Pio Pico Drive, Suite A, Carlsbad, CA 92008, USA. chris@b-alert.com
authors.forename: Chris
authors.initials: C
authors.lastname: Berka
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Levendowski
authors.forename: Michelle N
authors.initials: MN
authors.lastname: Lumicao
authors.forename: Alan
authors.initials: A
authors.lastname: Yau
authors.forename: Gene
authors.initials: G
authors.lastname: Davis
authors.forename: Vladimir T
authors.initials: VT
authors.lastname: Zivkovic
authors.forename: Richard E
authors.initials: RE
authors.lastname: Olmstead
authors.forename: Patrice D
authors.initials: PD
authors.lastname: Tremoulet
authors.forename: Patrick L
authors.initials: PL
authors.lastname: Craven
mesh_terms: D000704:Analysis of Variance; D001288:Attention / Q000502:physiology; D003071:Cognition / Q000502:physiology; D004569:Electroencephalography; D006801:Humans; D007858:Learning / Q000502:physiology; D008328:Man-Machine Systems; D008568:Memory / Q000502:physiology; D008606:Mental Processes / Q000502:physiology*; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D011340:Problem Solving; D011930:Reaction Time / Q000502:physiology; D013647:Task Performance and Analysis; D016526:Workload