Appriou, Aurélien

Kolodzienski, Léna

Lotte, Fabien

Roc, Aline

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Towards understanding the impact of mental task execution on user's state, experience and performances

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Towards understanding the impact of mental task execution onuser’s state, experience and performancesLéna Kolodzienski, Aline Roc, Aurélien Appriou, and Fabien LotteInria, LaBRI (CNRS, Univ. Bordeaux, BORDEAUX INP), Talence, FranceAbstract accepted for communication at JJC-ICON’2021, Grenoble, FranceInitially accepted for JJC-ICON’2020, Grenoble, France (canceled due to COVID-situation)AbstractImproving BCI user-training methods requires to understand the influence of user’s psychological traits andstates. To do so, recent studies have focused on user’s cognitive traits (e.g. attention or task-related abilities)[1] and state (e.g. mood or anxiety) and their effect on MI-BCI performance [2, 1]. Learning how to use aMI-BCI can be considered as a demanding task in term of cognitive resources [3]. According to the CognitiveLoad Theory, optimal learning occurs when the learning material requires a balanced amount of cognitiveworkload for the user [4]. Assessing users’ cognitive workload during MI-BCI training might then help adaptingthe amount of cognitive resources required by the task to users’ state evolution. Currently cognitive workloadin MI-BCI experimentations is mostly assessed with indirect and discrete subjective measures (e.g. NASA-TLX)[5, 2, 6] and must be complemented with objective and continuous direct measurements.We suggest a protocol in which users will, before the MI-BCI training, complete two different cognitivetasks involving a high and low cognitive load. Meanwhile, EEG activity would be recorded and used to build aclassifier to assess in real-time the cognitive load level of users during training. The second part of the studywill compare, between subjects, traditional instructional design with one based on compatibility between thefeedback and the MI task (i.e. transparent mapping) [7]. This should improve the sense of agency [7], reducetask-related cognitive load and, hopefully, allow users to learn faster than the control group.References[1] C. Jeunet, B. N’Kaoua, and F. Lotte, “Towards a cognitive model of mi-bci user training,” in 7th International BCI Conference, (Graz, Austria), 2017.[2] E. A. Felton, J. C. Williams, G. C. Vanderheiden, and R. G. Radwin, “Mental workload during brain–computer interface training,” Ergonomics, vol. 55,no. 5, pp. 526–537, 2012.[3] P. Gerjets, C. Walter, W. Rosenstiel, M. Bogdan, and T. O. Zander, “Cognitive state monitoring and the design of adaptive instruction in digitalenvironments: lessons learned from cognitive workload assessment using a passive brain-computer interface approach,” Frontiers in neuroscience,vol. 8, p. 385, 2014.[4] S. Puma and A. Tricot, “Cognitive load theory and working memory models,” Advances in Cognitive Load Theory: Rethinking Teaching, p. 41, 2019.[5] Z. Emami and T. Chau, “The effects of visual distractors on cognitive load in a motor imagery brain-computer interface,” Behavioural brain research,vol. 378, p. 112240, 2020.[6] S. G. Hart and L. E. Staveland, “Development of nasa-tlx (task load index): Results of empirical and theoretical research,” in Advances in psychology,vol. 52, pp. 139–183, Elsevier, 1988.[7] R. Vlek, J.-P. van Acken, E. Beursken, L. Roijendijk, and P. Haselager, “Bci and a user’s judgment of agency,” in Brain-Computer-Interfaces in theirethical, social and cultural contexts, pp. 193–202, Springer, 2014.1

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Towards understanding the impact of mental task execution on user's state, experience and performances

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