ReviewJournal of neural engineering2016
Integrating language models into classifiers for BCI communication: a review.
Review in Journal of neural engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- A comparison of stimulus types in online classification of the P300 speller using language models.PloS one · 2017Trial
- Near-optimal P300 speller performance using large language models: A multi-model analysis with performance bounds.PloS one · 2026Article
- Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface.Sensors (Basel, Switzerland) · 2025Review
- Large-scale foundation models and generative AI for BigData neuroscience.Neuroscience research · 2025Review
- Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention.Nature communications · 2024Article
- On the role of generative artificial intelligence in the development of brain-computer interfaces.BMC biomedical engineering · 2024Review
- Ethical issues raised by incorporating personalized language models into brain-computer interface communication technologies: a qualitative study of individuals with neurological disease.Disability and rehabilitation. Assistive technology · 2024Article
- Brain-computer interfaces and human factors: the role of language and cultural differences-Still a missing gap?Frontiers in human neuroscience · 2024Article
- Optimising non-invasive brain-computer interface systems for free communication between naïve human participants.Scientific reports · 2019Article
- Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation.Brain computer interfaces (Abingdon, England) · 2019Article
- Article
- A Multi-Context Character Prediction Model for a Brain-Computer Interface.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2018Article
- Brain-Computer Interface Spellers: A Review.Brain sciences · 2018Review
- Effects of simulated visual acuity and ocular motility impairments on SSVEP brain-computer interface performance: An experiment with Shuffle Speller.Brain computer interfaces (Abingdon, England) · 2018Article
- Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.Journal of neural engineering · 2017Article
- Eye motion triggered self-powered mechnosensational communication system using triboelectric nanogenerator.Science advances · 2017Article
- Modulation of Neural Activity during Guided Viewing of Visual Art.Frontiers in human neuroscience · 2017Article
- Online BCI Typing using Language Model Classifiers by ALS Patients in their Homes.Brain computer interfaces (Abingdon, England) · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectiveThe present review systematically examines the integration of language models to improve classifier performance in brain-computer interface (BCI) communication systems. APPROACH: The domain of natural language has been studied extensively in linguistics and has been used in the natural language processing field in applications including information extraction, machine translation, and speech recognition. While these methods have been used for years in traditional augmentative and assistive communication devices, information about the output domain has largely been ignored in BCI communication systems. Over the last few years, BCI communication systems have started to leverage this information through the inclusion of language models. MAIN
resultsAlthough this movement began only recently, studies have already shown the potential of language integration in BCI communication and it has become a growing field in BCI research. BCI communication systems using language models in their classifiers have progressed down several parallel paths, including: word completion; signal classification; integration of process models; dynamic stopping; unsupervised learning; error correction; and evaluation. SIGNIFICANCE: Each of these methods have shown significant progress, but have largely been addressed separately. Combining these methods could use the full potential of language model, yielding further performance improvements. This integration should be a priority as the field works to create a BCI system that meets the needs of the amyotrophic lateral sclerosis population.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.