ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2014
Utilizing a language model to improve online dynamic data collection in P300 spellers.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- ChatBCI, a P300 speller BCI with context-driven word prediction leveraging large language models, from concept to evaluation.Scientific reports · 2026Article
- Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.Journal of neural engineering · 2017Article
- A Synchronous Motor Imagery Based Neural Physiological Paradigm for Brain Computer Interface Speller.Frontiers in human neuroscience · 2017Article
- Online BCI Typing using Language Model Classifiers by ALS Patients in their Homes.Brain computer interfaces (Abingdon, England) · 2017Article
- Using the detectability index to predict P300 speller performance.Journal of neural engineering · 2016Article
- Integrating language models into classifiers for BCI communication: a review.Journal of neural engineering · 2016Review
- Probabilistic Simulation Framework for EEG-Based BCI Design.Brain computer interfaces (Abingdon, England) · 2016Article
- Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.Journal of neural engineering · 2015Article
- An auditory multiclass brain-computer interface with natural stimuli: Usability evaluation with healthy participants and a motor impaired end user.Frontiers in human neuroscience · 2014Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
P300 spellers provide a means of communication for individuals with severe physical limitations, especially those with locked-in syndrome, such as amyotrophic lateral sclerosis. However, P300 speller use is still limited by relatively low communication rates due to the multiple data measurements that are required to improve the signal-to-noise ratio of event-related potentials for increased accuracy. Therefore, the amount of data collection has competing effects on accuracy and spelling speed. Adaptively varying the amount of data collection prior to character selection has been shown to improve spelling accuracy and speed. The goal of this study was to optimize a previously developed dynamic stopping algorithm that uses a Bayesian approach to control data collection by incorporating a priori knowledge via a language model. Participants ( n = 17) completed online spelling tasks using the dynamic stopping algorithm, with and without a language model. The addition of the language model resulted in improved participant performance from a mean theoretical bit rate of 46.12 bits/min at 88.89% accuracy to 54.42 bits/min ( ) at 90.36% accuracy.
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Registered trials
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