MétaCan
Menu
Back to cohort
Record W1970346247 · doi:10.1145/1280720.1280879

NeuroFloat

2007· article· en· W1970346247 on OpenAlexaff
Steven J. Barnes, Meehae Song, Kyung Jae Lee, Diane Gromala, Chris Shaw

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceVisualizationVirtual realityHuman–computer interactionBrain–computer interfaceOptical head-mounted displayUser interfaceElectroencephalographyInterface (matter)Graphical user interfaceComputer graphics (images)Artificial intelligenceNeuroscienceOperating system

Abstract

fetched live from OpenAlex

We present a novel interactive system that uses electroencephalographic (EEG) signals obtained from the user as a primary input for navigation through an immersive real-time 3-D visualization of various regions of the human brain. This "NeuroFloat" system is delivered via a head-mounted display (HMD). Our primary goal in developing NeuroFloat was to create a system where the brain-computer interface (BCI) was intrinsically related to the virtual-reality (VR) environment that it interfaced with. Accordingly, at this early stage in the development of NeuroFloat, we chose to develop multiple VR environments that collectively represent a tour through the early stages of the user's own visual system.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.919
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same topicGaze Tracking and Assistive TechnologyFrench-language works237,207