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Identifying Cognitive Assistance with Mobile Electroencephalography: A Case Study with In-Situ Projections for Manual Assembly

Published:19 June 2018Publication History
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Abstract

Manual assembly at production is a mentally demanding task. With rapid prototyping and smaller production lot sizes, this results in frequent changes of assembly instructions that have to be memorized by workers. Assistive systems compensate this increase in mental workload by providing "just-in-time" assembly instructions through in-situ projections. The implementation of such systems and their benefits to reducing mental workload have previously been justified with self-perceived ratings. However, there is no evidence by objective measures if mental workload is reduced by in-situ assistance. In our work, we showcase electroencephalography (EEG) as a complementary evaluation tool to assess cognitive workload placed by two different assistive systems in an assembly task, namely paper instructions and in-situ projections. We identified the individual EEG bandwidth that varied with changes in working memory load. We show, that changes in the EEG bandwidth are found between paper instructions and in-situ projections, indicating that they reduce working memory compared to paper instructions. Our work contributes by demonstrating how design claims of cognitive demand can be validated. Moreover, it directly evaluates the use of assistive systems for delivering context-aware information. We analyze the characteristics of EEG as real-time assessment for cognitive workload to provide insights regarding the mental demand placed by assistive systems.

References

  1. Charles W Anderson and Zlatko Sijercic. 1996. Classification of EEG signals from four subjects during five mental tasks. In Solving engineering problems with neural networks: proceedings of the conference on engineering applications in neural networks (EANN'96). Turkey, 407--414.Google ScholarGoogle Scholar
  2. E. W. Anderson, K. C. Potter, L. E. Matzen, J. F. Shepherd, G. A. Preston, and C. T. Silva. 2011. A User Study of Visualization Effectiveness Using EEG and Cognitive Load. Computer Graphics Forum 30, 3 (2011), 791--800.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Alan D Baddeley and Graham Hitch. 1974. Working memory. Psychology of learning and motivation 8 (1974), 47--89.Google ScholarGoogle Scholar
  4. Robert J. Barry, Adam R. Clarke, Stuart J. Johnstone, Christopher A. Magee, and Jacqueline A. Rushby. 2007. EEG differences between eyes-closed and eyes-open resting conditions. Clinical Neurophysiology 118, 12 (2007), 2765 -- 2773.Google ScholarGoogle ScholarCross RefCross Ref
  5. Chris Berka, Daniel J. Levendowski, Milenko M. Cvetinovic, Miroslav M. Petrovic, Gene Davis, Michelle N. Lumicao, Vladimir T. Zivkovic, Miodrag V. Popovic, and Richard Olmstead. 2004. Real-Time Analysis of EEG Indexes of Alertness, Cognition, and Memory Acquired With a Wireless EEG Headset. International Journal of Human--Computer Interaction 17, 2 (2004), 151--170.Google ScholarGoogle ScholarCross RefCross Ref
  6. Jonas Blattgerste, Benjamin Strenge, Patrick Renner, Thies Pfeiffer, and Kai Essig. 2017. Comparing Conventional and Augmented Reality Instructions for Manual Assembly Tasks. In Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '17). ACM, New York, NY, USA, 75--82. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Karel A Brookhuis and Dick deWaard. 2002. On the assessment of (mental) workload and other subjective qualifications. Ergonomics 45, 14 (2002), 1026--1030.Google ScholarGoogle ScholarCross RefCross Ref
  8. Anne-Marie Brouwer, Maarten A Hogervorst, Jan BF Van Erp, Tobias Heffelaar, Patrick H Zimmerman, and Robert Oostenveld. 2012. Estimating workload using EEG spectral power and ERPs in the n-back task. Journal of neural engineering 9, 4 (2012), 045008.Google ScholarGoogle ScholarCross RefCross Ref
  9. Anne-Marie Brouwer, Maarten A Hogervorst, Jan B F van Erp, Tobias Heffelaar, Patrick H Zimmerman, and Robert Oostenveld. 2012. Estimating workload using EEG spectral power and ERPs in the n-back task. Journal of Neural Engineering 9, 4 (2012), 045008. http://stacks.iop.org/1741--2552/9/i=4/a=045008Google ScholarGoogle ScholarCross RefCross Ref
  10. Andreas Bulling and Thorsten O Zander. 2014. Cognition-aware computing. IEEE Pervasive Computing 13, 3 (2014), 80--83.Google ScholarGoogle ScholarCross RefCross Ref
  11. Christopher G Burns and Stephen H Fairclough. 2015. Use of auditory event-related potentials to measure immersion during a computer game. International Journal of Human-Computer Studies 73 (2015), 107--114.Google ScholarGoogle ScholarCross RefCross Ref
  12. Sebastian Büttner, Henrik Mucha, Markus Funk, Thomas Kosch, Mario Aehnelt, Sebastian Robert, and Carsten Röcker. 2017. The Design Space of Augmented and Virtual Reality Applications for Assistive Environments in Manufacturing: A Visual Approach. In Proceedings of the 10th ACM International Conference on PErvasive Technologies Related to Assistive Environments. ACM, New York, NY, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Sebastian Büttner, Oliver Sand, and Carsten Röcker. 2015. Extending the Design Space in Industrial Manufacturing Through Mobile Projection. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (MobileHCI '15). ACM, New York, NY, USA, 1130--1133. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Tim R.H. Cutmore and Daniel A. James. 1999. Identifying and reducing noise in psychophysiological recordings. International Journal of Psychophysiology 32, 2 (1999), 129 -- 150.Google ScholarGoogle Scholar
  15. Michael Doppelmayr, Wolfgang Klimesch, Th Pachinger, and B Ripper. 1998. Individual differences in brain dynamics: important implications for the calculation of event-related band power. Biological cybernetics 79, 1 (1998), 49--57.Google ScholarGoogle Scholar
  16. Matthieu Duvinage, Thierry Castermans, Mathieu Petieau, Thomas Hoellinger, Guy Cheron, and Thierry Dutoit. 2013. Performance of the Emotiv Epoc headset for P300-based applications. Biomedical engineering online 12, 1 (2013), 56.Google ScholarGoogle Scholar
  17. Mai ElKomy, Yomna Abdelrahman, Markus Funk, Tilman Dingler, Albrecht Schmidt, and Slim Abdennadher. 2017. ABBAS: AnAdaptive Bio-sensors BasedAssistive System. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '17). ACM, New York, NY, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. RandallWEngle, StephenWTuholski, James E Laughlin, and Andrew RA Conway. 1999. Working memory, short-term memory, and general fluid intelligence: a latent-variable approach. Journal of experimental psychology: General 128, 3 (1999), 309.Google ScholarGoogle ScholarCross RefCross Ref
  19. A. Fink, R.H. Grabner, C. Neuper, and A.C. Neubauer. 2005. EEG alpha band dissociation with increasing task demands. Cognitive Brain Research 24, 2 (2005), 252 -- 259.Google ScholarGoogle ScholarCross RefCross Ref
  20. Bruce J Fisch and Rainer Spehlmann. 1999. Fisch and Spehlmann's EEG primer: basic principles of digital and analog EEG. Elsevier Health Sciences.Google ScholarGoogle Scholar
  21. C. Ailie Fraser, Tovi Grossman, and George Fitzmaurice. 2017. WeBuild: Automatically Distributing Assembly Tasks Among Collocated Workers to Improve Coordination. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). ACM, New York, NY, USA, 1817--1830. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Markus Funk, Andreas Bächler, Liane Bächler, Thomas Kosch, Thomas Heidenreich, and Albrecht Schmidt. 2017. Working with Augmented Reality? A Long-Term Analysis of In-Situ Instructions at the Assembly Workplace. In Proceedings of the 10th ACM International Conference on PErvasive Technologies Related to Assistive Environments. ACM, New York, NY, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Markus Funk, Tilman Dingler, Jennifer Cooper, and Albrecht Schmidt. 2015. Stop Helping Me - I'm Bored! Why Assembly Assistance needs to be Adaptive. In Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Markus Funk, Thomas Kosch, ScottWGreenwald, and Albrecht Schmidt. 2015. A benchmark for interactive augmented reality instructions for assembly tasks. In Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia. ACM, 253--257. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Markus Funk, Thomas Kosch, and Albrecht Schmidt. 2016. Interactive Worker Assistance: Comparing the Effects of In-situ Projection, Head-mounted Displays, Tablet, and Paper Instructions. (2016), 934--939. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Markus Funk, Sven Mayer, and Albrecht Schmidt. 2015. Using In-Situ Projection to Support Cognitively Impaired Workers at the Workplace. In Proceedings of the 17th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '15). ACM, New York, NY, USA, 185--192. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Markus Funk, Alireza Sahami Shirazi, Sven Mayer, Lars Lischke, and Albrecht Schmidt. 2015. Pick from Here! - An Interactive Mobile Cart using In-Situ Projection for Order Picking. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Markus Funk and Albrecht Schmidt. 2015. Cognitive assistance in the workplace. IEEE Pervasive Computing 14, 3 (2015), 53--55.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Alan Gevins and Michael E Smith. 2003. Neurophysiological measures of cognitive workload during human-computer interaction. Theoretical Issues in Ergonomics Science 4, 1--2 (2003), 113--131.Google ScholarGoogle ScholarCross RefCross Ref
  30. Alan Gevins, Michael E Smith, Harrison Leong, Linda McEvoy, Susan Whitfield, Robert Du, and Georgia Rush. 1998. Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Human Factors: The Journal of the Human Factors and Ergonomics Society 40, 1 (1998), 79--91.Google ScholarGoogle ScholarCross RefCross Ref
  31. Alan Gevins, Michael E Smith, Linda McEvoy, and Daphne Yu. 1997. High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cerebral cortex 7, 4 (1997), 374--385.Google ScholarGoogle Scholar
  32. Pierre Gloor. 1969. Hans Berger on Electroencephalography. American Journal of EEG Technology 9, 1 (1969), 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  33. David Grimes, Desney S. Tan, Scott E. Hudson, Pradeep Shenoy, and Rajesh P.N. Rao. 2008. Feasibility and Pragmatics of Classifying Working Memory Load with an Electroencephalograph. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '08). ACM, New York, NY, USA, 835--844. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Eija Haapalainen, SeungJun Kim, Jodi F. Forlizzi, and Anind K. Dey. 2010. Psycho-physiological Measures for Assessing Cognitive Load. In Proceedings of the 12th ACM International Conference on Ubiquitous Computing (UbiComp'10). ACM, New York, NY, USA, 301--310. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Sandra G Hart and Lowell E Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances in psychology 52 (1988), 139--183.Google ScholarGoogle Scholar
  36. Suzana Herculano-Houzel. 2009. The human brain in numbers: a linearly scaled-up primate brain. Frontiers in human neuroscience 3 (2009), 31.Google ScholarGoogle Scholar
  37. Nina Hollender, Cristian Hofmann, Michael Deneke, and Bernhard Schmitz. 2010. Integrating cognitive load theory and concepts of human-computer interaction. Computers in Human Behavior 26, 6 (2010), 1278 -- 1288. Online Interactivity: Role of Technology in Behavior Change. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Ole Jensen, Jack Gelfand, John Kounios, and John E. Lisman. 2002. Oscillations in the Alpha Band (9--12 Hz) Increase with Memory Load during Retention in a Short-term Memory Task. Cerebral Cortex 12, 8 (2002), 877.Google ScholarGoogle ScholarCross RefCross Ref
  39. Ted J Kaptchuk. 2003. Effect of interpretive bias on research evidence. Bmj 326, 7404 (2003), 1453--1455.Google ScholarGoogle ScholarCross RefCross Ref
  40. Ivo Käthner, Selina CWriessnegger, Gernot R Müller-Putz, Andrea Kübler, and Sebastian Halder. 2014. Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain--computer interface. Biological psychology 102 (2014), 118--129.Google ScholarGoogle Scholar
  41. Z. A. Keirn and J. I. Aunon. 1990. A new mode of communication between man and his surroundings. IEEE Transactions on Biomedical Engineering 37, 12 (Dec 1990), 1209--1214.Google ScholarGoogle ScholarCross RefCross Ref
  42. Yoshifumi Kitamura, Yoshihisa Yamaguchi, Imamizu Hiroshi, Fumio Kishino, and Mitsuo Kawato. 2003. Things Happening in the Brain While Humans Learn to Use New Tools. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '03). ACM, New York, NY, USA, 417--424. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. W. Klimesch, H. Schimke, and G. Pfurtscheller. 1993. Alpha frequency, cognitive load and memory performance. Brain Topography 5, 3 (1993), 241--251.Google ScholarGoogle ScholarCross RefCross Ref
  44. Thomas Kosch, Yomna Abdelrahman, Markus Funk, and Albrecht Schmidt. 2017. One Size does not Fit All - Challenges of Providing Interactive Worker Assistance in Industrial Settings. Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing (2017), 6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Thomas Kosch, Mariam Hassib, Daniel Buschek, and Albrecht Schmidt. 2018. Look into My Eyes: Using Pupil Dilation to Estimate Mental Workload for Task Complexity Adaptation. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (CHI EA '18). ACM, New York, NY, USA, Article LBW617, 6 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Thomas Kosch, Mariam Hassib, and Albrecht Schmidt. 2016. The Brain Matters: A 3D Real-Time Visualization to Examine Brain Source Activation Leveraging Neurofeedback. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '16). ACM, New York, NY, USA, 1570--1576. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Thomas Kosch, Mariam Hassib, Paweł W. Wozniak, Daniel Buschek, and Florian Alt. 2018. Your Eyes Tell: Leveraging Smooth Pursuit for Assessing Cognitive Workload. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI'18). ACM, New York, NY, USA, Article 436, 13 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Thomas Kosch, Romina Kettner, Markus Funk, and Albrecht Schmidt. 2016. Comparing Tactile, Auditory, and Visual Assembly Error-Feedback for Workers with Cognitive Impairments. In Proceedings of the 18th international ACM SIGACCESS conference on Computers and accessibility. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Olave E. Krigolson, Chad C. Williams, Angela Norton, Cameron D. Hassall, and Francisco L. Colino. 2017. Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research. Frontiers in Neuroscience 11 (2017), 109.Google ScholarGoogle ScholarCross RefCross Ref
  50. III Lawrence J. Prinzel, Pope Alan T., Freeman Frederick G., Scerbo Mark W., and Mikulka Peter J. 2001. Empirical Analysis of EEG and ERPs for Pyschophysiological Adaptive Task Allocation. Technical Report. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Johnny Chung Lee and Desney S. Tan. 2006. Using a Low-cost Electroencephalograph for Task Classification in HCI Research. In Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology (UIST '06). ACM, New York, NY, USA, 81--90. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Fabien Lotte. 2014. A Tutorial on EEG Signal-processing Techniques for Mental-state Recognition in Brain--Computer Interfaces. Springer London, London, 133--161.Google ScholarGoogle Scholar
  53. F Lotte, L Bougrain, A Cichocki, M Clerc, M Congedo, A Rakotomamonjy, and F Yger. 2018. A review of classification algorithms for EEG-based brain--computer interfaces: a 10 year update. Journal of Neural Engineering 15, 3 (2018), 031005. http://stacks.iop.org/1741--2552/15/i=3/a=031005Google ScholarGoogle ScholarCross RefCross Ref
  54. F Lotte, M Congedo, A Lecuyer, F Lamarche, and B Arnaldi. 2007. A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering 4, 2 (2007), R1. http://stacks.iop.org/1741--2552/4/i=2/a=R01Google ScholarGoogle ScholarCross RefCross Ref
  55. Jaakko Malmivuo and Robert Plonsey. 1995. Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields. Oxford University Press, USA.Google ScholarGoogle Scholar
  56. S. G. Mason and G. E. Birch. 2003. A general framework for brain-computer interface design. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11, 1 (March 2003), 70--85.Google ScholarGoogle Scholar
  57. R. Matthews, P. J. Turner, N. J. McDonald, K. Ermolaev, T. M. Manus, R. A. Shelby, and M. Steindorf. 2008. Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 5871--5875.Google ScholarGoogle Scholar
  58. Bruce Mehler, Bryan Reimer, and Jeffery A Dusek. 2011. MIT AgeLab delayed digit recall task (n-back). Cambridge, MA: Massachusetts Institute of Technology (2011).Google ScholarGoogle Scholar
  59. Matthew W Miller, Jeremy C Rietschel, Craig G McDonald, and Bradley D Hatfield. 2011. A novel approach to the physiological measurement of mental workload. International Journal of Psychophysiology 80, 1 (2011), 75--78.Google ScholarGoogle ScholarCross RefCross Ref
  60. Christian Mühl, Camille Jeunet, and Fabien Lotte. 2014. EEG-based workload estimation across affective contexts. Frontiers in neuroscience 8 (2014).Google ScholarGoogle Scholar
  61. Tim R Mullen, Christian AE Kothe, Yu Mike Chi, Alejandro Ojeda, Trevor Kerth, Scott Makeig, Tzyy-Ping Jung, and Gert Cauwenberghs. 2015. Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Transactions on Biomedical Engineering 62, 11 (2015), 2553--2567.Google ScholarGoogle ScholarCross RefCross Ref
  62. Klaus-Robert Müller, Michael Tangermann, Guido Dornhege, Matthias Krauledat, Gabriel Curio, and Benjamin Blankertz. 2008. Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring. Journal of Neuroscience Methods 167, 1 (2008), 82 -- 90. Brain-Computer Interfaces (BCIs).Google ScholarGoogle ScholarCross RefCross Ref
  63. Luis Fernando Nicolas-Alonso and Jaime Gomez-Gil. 2012. Brain computer interfaces, a review. Sensors 12, 2 (2012), 1211--1279.Google ScholarGoogle ScholarCross RefCross Ref
  64. Vadim V. Nikulin, Guido Nolte, and Gabriel Curio. 2011. A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition. NeuroImage 55, 4 (2011), 1528 -- 1535.Google ScholarGoogle ScholarCross RefCross Ref
  65. N. Y. L. Oei, W. T. A. M. Everaerd, B. M. Elzinga, S. van Well, and B. Bermond. 2006. Psychosocial stress impairs working memory at high loads: An association with cortisol levels and memory retrieval. Stress 9, 3 (2006), 133--141. arXiv:http://dx.doi.org/10.1080/10253890600965773 PMID: 17035163.Google ScholarGoogle ScholarCross RefCross Ref
  66. Adrian M. Owen, Kathryn M. McMillan, Angela R. Laird, and Ed Bullmore. 2005. N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Human Brain Mapping 25, 1 (2005), 46--59.Google ScholarGoogle ScholarCross RefCross Ref
  67. R Parasuraman and J Beatty. 1980. Brain events underlying detection and recognition of weak sensory signals. Science 210, 4465 (1980), 80--83.Google ScholarGoogle Scholar
  68. Anat Perry, Nikolaus F. Troje, and Shlomo Bentin. 2010. Exploring motor system contributions to the perception of social information: Evidence from EEG activity in the mu/alpha frequency range. Social Neuroscience 5, 3 (2010), 272--284. arXiv:http://dx.doi.org/10.1080/17470910903395767 PMID: 20169504.Google ScholarGoogle ScholarCross RefCross Ref
  69. Gert Pfurtscheller and FH Lopes Da Silva. 1999. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical neurophysiology 110, 11 (1999), 1842--1857.Google ScholarGoogle Scholar
  70. R N Roy, S Charbonnier, A Campagne, and S Bonnet. 2016. Efficient mental workload estimation using task-independent EEG features. Journal of Neural Engineering 13, 2 (2016), 026019. http://stacks.iop.org/1741--2552/13/i=2/a=026019Google ScholarGoogle ScholarCross RefCross Ref
  71. Oliver Sand, Sebastian Büttner, Volker Paelke, and Carsten Röcker. 2016. smARt.Assembly -- Projection-Based Augmented Reality for Supporting Assembly Workers. Springer International Publishing, Cham, 643--652.Google ScholarGoogle Scholar
  72. Christian Scharinger, Alexander Soutschek, Torsten Schubert, and Peter Gerjets. 2017. Comparison of the working memory load in n-back and working memory span tasks by means of EEG frequency band power and P300 amplitude. Frontiers in human neuroscience 11 (2017).Google ScholarGoogle Scholar
  73. Menja Scheer, Heinrich H Bülthoff, and Lewis L Chuang. 2016. Steering demands diminish the early-P3, late-P3 and RON components of the event-related potential of task-irrelevant environmental sounds. Frontiers in human neuroscience 10 (2016).Google ScholarGoogle Scholar
  74. Daniela Schoofs, Diana Preuß, and Oliver T. Wolf. 2008. Psychosocial stress induces working memory impairments in an n-back paradigm. Psychoneuroendocrinology 33, 5 (2008), 643 -- 653.Google ScholarGoogle ScholarCross RefCross Ref
  75. A. Stipacek, R.H. Grabner, C. Neuper, A. Fink, and A.C. Neubauer. 2003. Sensitivity of human EEG alpha band desynchronization to different working memory components and increasing levels of memory load. Neuroscience Letters 353, 3 (2003), 193 -- 196.Google ScholarGoogle ScholarCross RefCross Ref
  76. Kirill Stytsenko, Evaldas Jablonskis, and Cosima Prahm. 2011. Evaluation of consumer EEG device Emotiv EPOC. In MEi: CogSci Conference 2011, Ljubljana.Google ScholarGoogle Scholar
  77. John Sweller. 1988. Cognitive load during problem solving: Effects on learning. Cognitive science 12, 2 (1988), 257--285.Google ScholarGoogle Scholar
  78. John Sweller. 1994. Cognitive load theory, learning difficulty, and instructional design. Learning and instruction 4, 4 (1994), 295--312.Google ScholarGoogle Scholar
  79. Anirudh Vallabhaneni, Tao Wang, and Bin He. 2005. Brain-Computer Interface. Springer US, Boston, MA, 85--121.Google ScholarGoogle Scholar
  80. Boris M. Velichkovsky and John Paulin Hansen. 1996. New Technological Windows into Mind: There is More in Eyes and Brains for Human-computer Interaction. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '96). ACM, New York, NY, USA, 496--503. Google ScholarGoogle ScholarDigital LibraryDigital Library
  81. John D Wells, Damon E Campbell, Joseph S Valacich, and Mauricio Featherman. 2010. The effect of perceived novelty on the adoption of information technology innovations: a risk/reward perspective. Decision Sciences 41, 4 (2010), 813--843.Google ScholarGoogle Scholar
  82. Brenda Wiederhold and Giuseppe Riva. 2013. Annual Review of CyberTherapy and Telemedicine: Positive Technology and Health Engagement for Healthy Living and Active Ageing.Google ScholarGoogle Scholar
  83. Jonathan R Wolpaw, Niels Birbaumer, Dennis J McFarland, Gert Pfurtscheller, and Theresa M Vaughan. 2002. Braincomputer interfaces for communication and control. Clinical Neurophysiology 113, 6 (2002), 767 -- 791.Google ScholarGoogle ScholarCross RefCross Ref
  84. Mark S Young and Neville A Stanton. 2002. Malleable attentional resources theory: a new explanation for the effects of mental underload on performance. Human factors 44, 3 (2002), 365--375.Google ScholarGoogle Scholar
  85. Thorsten O. Zander, Lena M. Andreessen, Angela Berg, Maurice Bleuel, Juliane Pawlitzki, Lars Zawallich, Laurens R. Krol, and Klaus Gramann. 2017. Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving. Frontiers in Human Neuroscience 11 (2017), 78.Google ScholarGoogle ScholarCross RefCross Ref
  86. Ferdinand Rudolf Hendrikus Zijlstra. 1993. Efficiency in work behaviour: A design approach for modern tools. (1993).Google ScholarGoogle Scholar

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