Lotte, Fabien

Roc, Aline

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Reviewing open challenges in Mental Tasks (MT-)based BCI user training

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HAL Id: hal-03065258https://hal.inria.fr/hal-03065258Submitted on 5 Jan 2021HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Reviewing open challenges in Mental Tasks (MT-)basedBCI user trainingAline Roc, Fabien LotteTo cite this version:Aline Roc, Fabien Lotte. Reviewing open challenges in Mental Tasks (MT-)based BCI user training.Virtual CORTICO days, Oct 2020, Virtual, France. ￿hal-03065258￿Reviewing open challenges in Mental Tasks (MT-)based BCI user trainingAline Roc and Fabien Lottealine.roc@inria.frInria, LaBRI (CNRS, Univ. Bordeaux, BORDEAUX INP), Talence, FranceAbstract accepted for an oral communication at virtual CORTICO days (2020)Initially accepted for JJC-ICON’2020, Grenoble, France (canceled due to COVID-situation)Schedule of the virtual day: https://www.cortico.fr/2020/10/13/demi-journee-cortico-2020/AbstractTraditional MI-BCI training programs could be further improved [1]. This can be donethrough guidelines [2]; user-centered design [3] and adaptive systems [4]. We conducteda literature review on several aspects of MI-BCI user-training, e.g. environment, cognitivesupport, exercises. Here, we highlight the elements that we identified as not sufficientlyexplored or formalized yet.Concerning the training environment, these challenges include:a) balancing between a relevant control task [5], a motivating environment, and theassociated risk of imposing cognitive overload [6, 7, 8];b) studying social aspects of training, e.g. feedback content [9].Concerning instructions and cognitive support, it includes:a) introducing the BCI to foster users’ understanding and engagement;b) evaluating the learnability/memorability of the MI-BCI skills and instructions [10];c) assessing users’ strategies [11];d) selecting user-centered task(s) [12];e) providing guidance based on user-only metrics instead of classification accuracy [13].Current challenges for MI-BCI exercise design include:a) implementing varied exercises instead of the only same one [1];b) studying indirect/incidental MI-BCI training tasks, e.g. mindfulness meditation [14],physical preparation [15];c) assessing online learners’ states [16] and progress to provide adapted/adaptive envi-ronment (e.g. manipulating timing [17]);d) exploring the suitable parameters of trials (e.g. order, duration, number [18]) andtheir influences on users’ states and performances.1References[1] R. Chavarriaga, M. Fried-Oken, S. Kleih, F. Lotte, and R. Scherer, “Heading for new shores! overcoming pitfalls in BCI design,” Brain-ComputerInterfaces, vol. 4, no. 1-2, pp. 60–73, 2016.[2] F. Lotte, F. Larrue, and C. Mühl, “Flaws in current human training protocols for spontaneous brain-computer interfaces: lessons learnedfrom instructional design,” Frontiers in Human Neuroscience, vol. 7, p. 568, 2013.[3] S. C. Kleih and A. Kübler, “Why user-centered design is relevant for brain–computer interfacing and how it can be implemented in studyprotocols,” in Brain–Computer Interfaces Handbook, pp. 585–594, CRC Press, 2018.[4] J. Mladenović, J. Mattout, and F. Lotte, “A generic framework for adaptive eeg-based bci training and operation,” CRC Press, 2018.[5] 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 intheir ethical, social and cultural contexts, pp. 193–202, Springer, 2014.[6] J. Mladenović, J. Frey, M. Bonnet-Save, J. Mattout, and F. Lotte, “The impact of flow in an eeg-based brain computer interface,” in 7thInternational BCI Conference, (Graz, Austria), 2017.[7] S. Enriquez-Geppert, R. J. Huster, and C. S. Herrmann, “Eeg-neurofeedback as a tool to modulate cognition and behavior: a review tutorial,”Frontiers in human neuroscience, vol. 11, p. 51, 2017.[8] Z. Emami and T. Chau, “The effects of visual distractors on cognitive load in a motor imagery brain-computer interface,” Behavioural brainresearch, vol. 378, p. 112240, 2020.[9] L. Pillette, C. Jeunet, B. Mansencal, R. N’kambou, B. N’Kaoua, and F. Lotte, “A physical learning companion for mental-imagery bci usertraining,” International Journal of Human-Computer Studies, vol. 136, p. 102380, 2020.[10] E. Loup-Escande, F. Lotte, G. Loup, and A. Lécuyer, “User-centered bci videogame design,” Handbook of digital games and entertainmenttechnologies, pp. 225–250, 2017.[11] J. Bismuth, F. Vialatte, and J.-P. Lefaucheur, “Relieving peripheral neuropathic pain by increasing the power-ratio of low-β over high-β activities in the central cortical region with eeg-based neurofeedback: Study protocol for a controlled pilot trial (smrpain study),”Neurophysiologie Clinique, vol. 50, no. 1, pp. 5–20, 2020.[12] A. Eliseyev and T. Aksenova, “Personalized adaptive instruction design (paid) for brain–computer interface using reinforcement learningand deep learning: simulated data study,” Brain-Computer Interfaces, vol. 6, no. 1-2, pp. 36–48, 2019.[13] F. Lotte and C. Jeunet, “Defining and quantifying users’ mental imagery-based bci skills: a first step,” Journal of neural engineering, vol. 15,no. 4, p. 046030, 2018.[14] L.-F. Tan, Z. Dienes, A. Jansari, and S.-Y. Goh, “Effect of mindfulness meditation on brain–computer interface performance,” Consciousnessand cognition, vol. 23, pp. 12–21, 2014.[15] S. C. Wriessnegger, C. Brunner, and G. R. Müller-Putz, “Frequency specific cortical dynamics during motor imagery are influenced by priorphysical activity,” Frontiers in psychology, vol. 9, p. 1976, 2018.[16] 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.[17] L. George, L. Bonnet, and A. Lécuyer, “Freeze the bci until the user is ready: a pilot study of a bci inhibitor,” in 5th InternationalBrain-Computer Interface Workshop, 2011.[18] V. Rozand, F. Lebon, P. J. Stapley, C. Papaxanthis, and R. Lepers, “A prolonged motor imagery session alter imagined and actual movementdurations: potential implications for neurorehabilitation,” Behavioural Brain Research, vol. 297, pp. 67–75, 2016.2

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Reviewing open challenges in Mental Tasks (MT-)based BCI user training

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