ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2017
Recursive Bayesian Coding for BCIs.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Human visual skills for brain-computer interface use: a tutorial.Disability and rehabilitation. Assistive technology · 2020Review
- SSVEP BCI and Eye Tracking Use by Individuals With Late-Stage ALS and Visual Impairments.Frontiers in human neuroscience · 2020Article
- Behind the Scenes of Noninvasive Brain-Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter.Perspectives of the ASHA special interest groups · 2019Article
- New and emerging access technologies for adults with complex communication needs and severe motor impairments: State of the science.Augmentative and alternative communication (Baltimore, Md. : 1985) · 2019Review
- Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation.Brain computer interfaces (Abingdon, England) · 2019Article
- Optimal Query Selection Using Multi-Armed Bandits.IEEE signal processing letters · 2018Article
- Ethical Considerations in Ending Exploratory Brain-Computer Interface Research Studies in Locked-in Syndrome.Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees · 2018Article
- On Analysis of Active Querying for Recursive State Estimation.IEEE signal processing letters · 2018Article
- Vigilance state fluctuations and performance using brain-computer interface for communication.Brain computer interfaces (Abingdon, England) · 2018Article
- 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
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Authors and funding
7 authors.
Funding
Abstract
Brain-Computer Interfaces (BCIs) seek to infer some task symbol, a task relevant instruction, from brain symbols, classifiable physiological states. For example, in a motor imagery robot control task a user would indicate their choice from a dictionary of task symbols (rotate arm left, grasp, etc.) by selecting from a smaller dictionary of brain symbols (imagined left or right hand movements). We examine how a BCI infers a task symbol using selections of brain symbols. We offer a recursive Bayesian decision framework which incorporates context prior distributions (e.g., language model priors in spelling applications), accounts for varying brain symbol accuracy and is robust to single brain symbol query errors. This framework is paired with Maximum Mutual Information (MMI) coding which maximizes a generalization of ITR. Both are applicable to any discrete task and brain phenomena (e.g., P300, SSVEP, MI). To demonstrate the efficacy of our approach we perform SSVEP "Shuffle" Speller experiments and compare our recursive coding scheme with traditional decision tree methods including Huffman coding. MMI coding leverages the asymmetry of the classifier's mistakes across a particular user's SSVEP responses; in doing so it offers a 33% increase in letter accuracy though it is 13% slower in our experiment.
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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.