Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design
Open accessAbstract
While recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI) often rely on mutual learning efforts by the user and the machine, with BCI users learning to produce stable ElectroEncephaloGraphy (EEG) patterns (spontaneous BCI control being widely acknowledged as a skill) while the computer learns to automatically recognize these EEG patterns, using signal processing. Most research so far was focused on signal processing, mostly neglecting the human in the loop. However, how well the user masters the BCI skill is also a key element explaining BCI robustness. Indeed, if the user is not able to produce stable and distinct EEG patterns, then no signal processing algorithm would be able to recognize them. Unfortunately, despite the importance of BCI training protocols, they have been scarcely studied so far, and used mostly unchanged for years. In this paper, we advocate that current human training approaches for spontaneous BCI are most likely inappropriate. We notably study instructional design literature in order to identify the key requirements and guidelines for a successful training procedure that promotes a good and efficient skill learning. This literature study highlights that current spontaneous BCI user training procedures satisfy very few of these requirements and hence are likely to be suboptimal. We therefore identify the flaws in BCI training protocols according to instructional design principles, at several levels: in the instructions provided to the user, in the tasks he/she has to perform, and in the feedback provided. For each level, we propose new research directions that are theoretically expected to address some of these flaws and to help users learn the BCI skill more efficiently.
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The authors' results are in line with the emerging belief about the need for more stimulating BCI training contexts.
“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 [53]
Various psychological factors, such as motivation, attention, and stress, have been implicated in these negative effects, which are quite frequent in MI BCI operation.
“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 [53]
Another contributing factor to successful SMR enhancement might have been the implementation of an "incremental learning" approach where open-loop, closed-loop, and application training followed each other throughout training.
“Related to this, another contributing factor to successful SMR enhancement might have been that we have implemented an “incremental learning” approach as advocated in [53] and shown in [69], where open-loop, closed-loop, and application training (tasks of increasing difficulty) followed each other throughout training.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [53]
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- Authors
- Lotte, F., Larrue, F., & Mühl, C.
- Affiliations
- Centre Inria de l'université de Bordeaux (FR)
- Corresponding author
- Lotte, Fabien
- Venue
- Frontiers in Human Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2013
- Volume
- 7
- Issue
- 568
- Pages
- 568-568
- ISSN
- 1662-5161 (ISSN-L)
- PMC
- PMC3775130
- OpenAlex
- W1974371581
- Cited by
- 307
- References
- 56
- Citations by year
- 2026: 13 · 2025: 24 · 2024: 24 · 2023: 13 · 2022: 15 · 2021: 29 · 2020: 34 · 2019: 22 · 2018: 32 · 2017: 22 · 2016: 32 · 2015: 32 · 2014: 15
- FWCI
- 14.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Human Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
- Keywords
- mental imagery, instructional design, skill acquisition, EEG signal recognition, user feedback, training instructions, mental state recognition
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