ArticleJournal of neural engineering2015
Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study.
Article in Journal of neural engineering, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- Toward the Clinical Translation of Implantable Brain-Computer Interfaces for Motor Impairment: Research Trends and Outcome Measures.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Pooled it
- Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review.Biomedical engineering online · 2021Pooled it
- Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals.Journal of neurophysiology · 2018Trial
- High performance communication by people with paralysis using an intracortical brain-computer interface.eLife · 2017Trial
- An Immersive P300 Brain-Computer Interface Based on 3D Morphological Stimuli and Self-Adaptive Bayesian Linear Discriminant Analysis.Biomimetics (Basel, Switzerland) · 2026Article
- Exploring the 'EarSwitch' concept: a novel ear based control method for assistive technology.Journal of neuroengineering and rehabilitation · 2024Article
- Brain-Computer Interfaces, Completely Locked-In State in Neurodegenerative Diseases, and End-of-Life Decisions.Journal of bioethical inquiry · 2024Article
- Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have.Journal of clinical medicine · 2023Review
- Language Model-Guided Classifier Adaptation for Brain-Computer Interfaces for Communication.Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics · 2022Article
- The science and engineering behind sensitized brain-controlled bionic hands.Physiological reviews · 2022Review
- Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions.Frontiers in human neuroscience · 2021Review
- Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling.Sensors (Basel, Switzerland) · 2020Article
- An ERP-based BCI with peripheral stimuli: validation with ALS patients.Cognitive neurodynamics · 2020Article
- Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability.Sensors (Basel, Switzerland) · 2019Review
- Real-time classification of auditory sentences using evoked cortical activity in humans.Journal of neural engineering · 2018Article
- Brain-Computer Interfaces for Augmentative and Alternative Communication: A Tutorial.American journal of speech-language pathology · 2018Review
- Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.Journal of neural engineering · 2017Article
- Online BCI Typing using Language Model Classifiers by ALS Patients in their Homes.Brain computer interfaces (Abingdon, England) · 2017Article
- A Synchronous Motor Imagery Based Neural Physiological Paradigm for Brain Computer Interface Speller.Frontiers in human neuroscience · 2017Article
- Using the detectability index to predict P300 speller performance.Journal of neural engineering · 2016Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThe P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography (EEG) data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio (SNR) of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred. APPROACH: We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute SNR of a user's EEG data. We further enhanced the algorithm by incorporating information about the user's language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (DS) (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation. MAIN
resultsResults from online testing of the DS algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/min (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the DS algorithms. SIGNIFICANCE: We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.
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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.