Trial reportJournal of neural engineering2013
Offline analysis of context contribution to ERP-based typing BCI performance.
Trial report in Journal of neural engineering, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Target-Related Alpha Attenuation in a Brain-Computer Interface Rapid Serial Visual Presentation Calibration.Frontiers in human neuroscience · 2022Article
- Ten-Hour Stable Noninvasive Brain-Computer Interface Realized by Semidry Hydrogel-Based Electrodes.Research (Washington, D.C.) · 2022Article
- Feedback Related Potentials for EEG-Based Typing Systems.Frontiers in human neuroscience · 2021Article
- INCORPORATING TEMPORAL DEPENDENCY ON ERP BASED BCI.Proceedings. IEEE International Symposium on Biomedical Imaging · 2018Article
- EEG-assisted Modulation of Sound Sources in the Auditory Scene.Biomedical signal processing and control · 2018Article
- An Active RBSE Framework to Generate Optimal Stimulus Sequences in a BCI for Spelling.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2017Article
- Recursive Bayesian Coding for BCIs.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2017Article
- Spatio-Temporal EEG Models for Brain Interfaces.Signal processing · 2017Article
- Online BCI Typing using Language Model Classifiers by ALS Patients in their Homes.Brain computer interfaces (Abingdon, England) · 2017Article
- 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
- Word-level language modeling for P300 spellers based on discriminative graphical models.Journal of neural engineering · 2015Article
- Brain-computer interface users speak up: the Virtual Users' Forum at the 2013 International Brain-Computer Interface Meeting.Archives of physical medicine and rehabilitation · 2015Article
- Teaching mindfulness meditation to adults with severe speech and physical impairments: An exploratory study.Neuropsychological rehabilitation · 2015Article
- Noninvasive brain-computer interfaces for augmentative and alternative communication.IEEE reviews in biomedical engineering · 2014Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveWe aim to increase the symbol rate of electroencephalography (EEG) based brain-computer interface (BCI) typing systems by utilizing context information. APPROACH: Event related potentials (ERP) corresponding to a stimulus in EEG can be used to detect the intended target of a person for BCI. This paradigm is widely utilized to build letter-by-letter BCI typing systems. Nevertheless currently available BCI typing systems still require improvement due to low typing speeds. This is mainly due to the reliance on multiple repetitions before making a decision to achieve higher typing accuracy. Another possible approach to increase the speed of typing while not significantly reducing the accuracy of typing is to use additional context information. In this paper, we study the effect of using a language model (LM) as additional evidence for intent detection. Bayesian fusion of an n-gram symbol model with EEG features is proposed, and a specifically regularized discriminant analysis ERP discriminant is used to obtain EEG-based features. The target detection accuracies are rigorously evaluated for varying LM orders, as well as the number of ERP-inducing repetitions. MAIN
resultsThe results demonstrate that the LMs contribute significantly to letter classification accuracy. For instance, we find that a single-trial ERP detection supported by a 4-gram LM may achieve the same performance as using 3-trial ERP classification for the non-initial letters of words. SIGNIFICANCE: Overall, the fusion of evidence from EEG and LMs yields a significant opportunity to increase the symbol rate of a BCI typing system.
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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.