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Experimental setup and results of independent component analysis (ICA) and source reconstruction

2014· article· en· W2245078158 on OpenAlexaboutno aff
Nikola Vukovic

Bibliographic record

VenueFigshare · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsIndependent component analysisComponent (thermodynamics)Computer scienceComponent analysisArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Experimental setup and results of independent component analysis (ICA) and source reconstruction. (A) EEG was recorded using a 128-channel recording cap based on the 5% positioning system. Marked on the illustration are the two clusters of electrodes used in subsequent sensor analyses.On each trial, a participant would see a fixation cross for 500ms, followed by an action or abstract prime word (L1 or L2) displayed for 150ms. After a 700ms blank screen, the probe action word (L1 or L2) would appear for 150ms. (B) Topographic maps for the two mu rhythm ICA clusters. For each cluster, individual participant components are shown, as well as the cluster average topography. Both mu clusters exhibited the characteristic scalp map distributions and spectral profiles. (C) Source localization was performed on the ICA components via dipole fitting. Equivalent dipole density (in dipoles per mm3) is plotted for the two mu clusters on slices of the Montreal Neurological Institute (MNI) MRI template brain image, using standard MNI coordinates.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.997

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)0.0040.000

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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.

Study designOther design
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
Published2014
Admission routes1
Has abstractyes

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