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Record W2124856079 · doi:10.1109/iembs.2008.4649364

A comparison between a matrix-based and a region-based P300 speller paradigms for brain-computer interface

2008· article· en· W2124856079 on OpenAlex
Reza Fazel-Rezai, Kamyar Abhari

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBrain–computer interfaceComputer scienceArtificial intelligenceSpeech recognitionInterface (matter)PerceptionCharacter (mathematics)Natural language processingPattern recognition (psychology)ElectroencephalographyPsychologyMathematics

Abstract

fetched live from OpenAlex

A brain-computer interface (BCI) is a system that conveys messages and commands directly from the human brain to a computer. The BCI system described in this work is based on P300 wave. The P300 is a positive peak of an event-related potential (ERP) that happens 300 ms after a stimulus. One of the most well-known and widely-used P300 applications is P300 speller designed by Farwell-Donchin in 1988. The Farwell-Donchin paradigm has been a benchmark in P300 BCI. In this paradigm, a 6x6 matrix of letters and numbers is displayed and subject focuses on a target character while rows and columns of characters flash. By detecting P300 for one row and one column, the target character can be identified. In this paper, it is shown that there is a human perceptual error in Farwell-Donchin paradigm. To remove this error, a new region-based paradigm is presented. Using experimental results, it is shown that the new paradigm has several advantages and it achieves a better accuracy compared to the Farwell-Donchin paradigm.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.079
GPT teacher head0.336
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations48
Published2008
Admission routes1
Has abstractyes

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