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Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study
Open accessAbstract
Objective. While promising, electroencephaloraphy based brain–computer interfaces (BCIs) are barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to what extent standard training protocols impact users’ motor imagery based BCI (MI-BCI) control performance. Approach. We performed two experiments. The first consisted in evaluating the efficiency of a standard BCI training protocol for the acquisition of non-BCI related skills in a BCI-free context, which enabled us to rule out the possible impact of BCIs on the training outcome. Thus, participants ( N = 54) were asked to perform simple motor tasks. The second experiment was aimed at measuring the correlations between motor tasks and MI-BCI performance. The ten best and ten worst performers of the first study were recruited for an MI-BCI experiment during which they had to learn to perform two MI tasks. We also assessed users’ spatial ability and pre-training μ rhythm amplitude, as both have been related to MI-BCI performance in the literature. Main results. Around 17% of the participants were unable to learn to perform the motor tasks, which is close to the BCI illiteracy rate. This suggests that standard training protocols are suboptimal for skill teaching. No correlation was found between motor tasks and MI-BCI performance. However, spatial ability played an important role in MI-BCI performance. In addition, once the spatial ability covariable had been controlled for, using an ANCOVA, it appeared that participants who faced difficulty during the first experiment improved during the second while the others did not. Significance. These studies suggest that (1) standard MI-BCI training protocols are suboptimal for skill teaching, (2) spatial ability is confirmed as impacting on MI-BCI performance, and (3) when faced with difficult pre-training, subjects seemed to explore more strategies and therefore learn better.
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“Our results are in line with the emerging belief about the need for more stimulating BCI training contexts [47,53,66].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [66]
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“Various psychological factors (such as motivation, attention, and stress) have been implicated in these negative effects [2,47,53,66–68], which unfortunately are quite frequent in MI BCI operation.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [66]
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Metadata
- Authors
- Jeunet, C., Jahanpour, E., & Lotte, F.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Université de Bordeaux (FR)
- Centre Inria de l'université de Bordeaux (FR)
- Corresponding author
- Jeunet, Camille
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2016-05-11
- Volume
- 13
- Issue
- 3
- Pages
- 036024
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2365862230
- Cited by
- 192
- References
- 49
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 17 · 2023: 17 · 2022: 15 · 2021: 30 · 2020: 28 · 2019: 23 · 2018: 20 · 2017: 8 · 2016: 4
- FWCI
- 11.63
- Type
- journal article
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Arts and Humanities › Philosophy › Free Will and Agency
- Keywords
- brain-computer interface, motor imagery, spatial ability, motor skill learning, BCI illiteracy, mu rhythm, EEG-based control, learning strategies
- MeSH terms
- Adult, Algorithms, Electroencephalography, Female, Humans, Imagination, Learning, Male, Motor Skills, Psychomotor Performance, Signal Processing, Computer-Assisted, Space Perception, Reproducibility of Results, Young Adult, Brain-Computer Interfaces, Spatial Navigation
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