A fully on-line adaptive BCI
Abstract
A viable fully on-line adaptive brain computer interface (BCI) is introduced. On-line experiments with nine naive and able-bodied subjects were carried out using a continuously adaptive BCI system. The data were analyzed and the viability of the system was studied. The BCI was based on motor imagery, the feature extraction was performed with an adaptive autoregressive model and the classifier used was an adaptive quadratic discriminant analysis. The classifier was on-line updated by an adaptive estimation of the information matrix (ADIM). The system was also able to provide continuous feedback to the subject. The success of the feedback was studied analyzing the error rate and mutual information of each session and this analysis showed a clear improvement of the subject's control of the BCI from session to session.
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Evidence that co-adaptive MI BCIs might also be able to promote and increase the ability of users to voluntarily modulate their brain signals (subject learning) is scarce, most often indirect and rather inconclusive.
“Evidence that co-adaptive MI BCIs might also be able to promote and increase the ability of the users to voluntary modulate their brain signals (subject learning) is, in fact, scarce, most often indirect and rather inconclusive.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [27]
Mutual learning has been claimed mostly on the grounds of adequate and improved BCI classification accuracy or application performances.
“Indeed, mutual learning has been claimed mostly on the grounds of adequate and improved BCI classification accuracy [25,27,30–35] or application performances [36,37].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [27]
Other training studies claim learnability in BCIs only on the grounds of improved online classification accuracy or application performances.
“Secondly, other training studies claim learnability in BCIs only on the grounds of improved online classification accuracy [9,27,30,31,33,35,51] or application performances [36,37].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [27]
Lack of robustness is a well-known issue of all BCI paradigms and has been associated with the nonstationarity of brain signals.
“Lack of robustness is a well-known issue of all BCI paradigms and has been associated to the nonstationarity of brain signals [18,25,27].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [27]
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- Authors
- Vidaurre, C., Schloogl, A., Cabeza, R., Scherer, R., & Pfurtscheller, G.
- Affiliations
- Universidad de Navarra (ES)
- Graz University of Technology (AT)
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2006-06
- Volume
- 53
- Issue
- 6
- Pages
- 1214-1219
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2111208616
- Cited by
- 195
- References
- 33
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 4 · 2023: 6 · 2022: 4 · 2021: 6 · 2020: 6 · 2019: 9 · 2018: 9 · 2017: 6 · 2016: 8 · 2015: 12 · 2014: 10 · 2013: 17 · 2012: 12
- FWCI
- 7.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
- 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 › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- brain-computer interface, online adaptation, motor imagery, adaptive autoregressive model, continuous feedback, mutual information, naive users, error rate
- MeSH terms
- Algorithms, Artificial Intelligence, Brain, Electroencephalography, Feedback, Humans, Imagination, Online Systems, Pattern Recognition, Automated, User-Computer Interface, Evoked Potentials, Motor
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