ArticleBrain computer interfaces (Abingdon, England)2017
Online BCI Typing using Language Model Classifiers by ALS Patients in their Homes.
Article in Brain computer interfaces (Abingdon, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
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Who cites it
12 citing papers in PubMed, 30 citations in OpenAlex.
- Neurodetector: EEG-Based Cognitive Assessment Using Event-Related Potentials as a Virtual Switch.Brain sciences · 2025Article
- Towards Predictive Communication: The Fusion of Large Language Models and Brain-Computer Interface.Sensors (Basel, Switzerland) · 2025Review
- Article
- Brain-Computer interface control of stepping from invasive electrocorticography upper-limb motor imagery in a patient with quadriplegia.Frontiers in human neuroscience · 2022Article
- Extending Brain-Computer Interface Access with a Multilingual Language Model in the P300 Speller.Brain computer interfaces (Abingdon, England) · 2022Article
- Implantable brain-computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury.Brain communications · 2021Article
- Brain-computer interfaces for amyotrophic lateral sclerosis.Muscle & nerve · 2020Review
- SSVEP BCI and Eye Tracking Use by Individuals With Late-Stage ALS and Visual Impairments.Frontiers in human neuroscience · 2020Article
- P300-Based Brain-Computer Interface Speller: Usability Evaluation of Three Speller Sizes by Severely Motor-Disabled Patients.Frontiers in human neuroscience · 2020Article
- Improving P300 Spelling Rate using Language Models and Predictive Spelling.Brain computer interfaces (Abingdon, England) · 2018Article
- Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.Computational intelligence and neuroscience · 2018Article
- The Sixth International Brain-Computer Interface Meeting: Advances in Basic and Clinical Research.Brain computer interfaces (Abingdon, England) · 2017Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The P300 speller is a common brain-computer interface system that can provide a means of communication for patients with amyotrophic lateral sclerosis (ALS). Recent studies have shown that incorporating language information in signal classification can improve system performance, but they have largely been tested on healthy volunteers in a laboratory setting. The goal of this study was to demonstrate the functionality of the P300 speller system with language models when used by ALS patients in their homes. Six ALS patients with functional ratings ranging from two to 28 participated in this study. All subjects had improved offline performance when using a language model and five subjects were able to type at least six characters per minute with over 84% accuracy in online sessions. The results of this study indicate that the improvements in performance using language models in the P300 speller translate into the ALS population, which could help to make it a viable assistive device.
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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.