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Article Dans Une Revue Journal of Neural Engineering Année : 2020

Standardization of Protocol Design for User Training in EEG-based Brain-Computer Interface

Jelena Mladenović
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Résumé

Brain-computer interfaces (BCIs) are systems that enable a personto interact with a machine using only neural activity. Such interaction canbe non-intuitive for the user hence training methods are developed to increaseone’s understanding, confidence and motivation, which would in parallel increasesystem performance. To clearly address the current issues in the BCI usertraining protocol design, here it is divided intointroductoryperiod and BCIinteractionperiod. First, theintroductoryperiod (before BCI interaction) mustbe considered as equally important as the BCI interaction for user training. Tosupport this claim, a review of papers show that BCI performance can dependon the methodologies presented in such introductory period. To standardize itsdesign, the literature from human-computer interaction (HCI) is adjusted to theBCI context. Second, during the user-BCI interaction, the interface can takea large spectrum of forms (2D, 3D, size, color etc.) and modalities (visual,auditory or haptic etc.) without following any design standard or guidelines.Namely, studies that explore perceptual affordance on neural activity show thatmotor neurons can be triggered from a simple observation of certain objects, anddepending on objects’ properties (size, location etc.) neural reactions can varygreatly. Surprisingly, the effects of perceptual affordance were not investigatedin the BCI context. Both inconsistent introductions to BCI as well as variableinterface designs make it difficult to reproduce experiments, predict their outcomesand compare results between them. To address these issues, a protocol designstandardization for user training is proposed.
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hal-03133558 , version 1 (06-02-2021)

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  • HAL Id : hal-03133558 , version 1

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Jelena Mladenović. Standardization of Protocol Design for User Training in EEG-based Brain-Computer Interface. Journal of Neural Engineering, 2020. ⟨hal-03133558⟩
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