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Record W2045434413 · doi:10.1364/oe.16.001029

Complex wavelets applied to diffuse optical spectroscopy for brain activity detection

2008· article· en· W2045434413 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOptics Express · 2008
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsPolytechnique MontréalÉcole de Technologie Supérieure
FundersCanadian Institutes of Health Research
KeywordsWaveletDiffuse optical imagingComputer scienceStimulus (psychology)Artificial intelligenceOpticsPattern recognition (psychology)Wavelet transformComputer visionSignal processingPhysicsPsychologyIterative reconstructionTelecommunications

Abstract

fetched live from OpenAlex

The analysis of diffuse optical imaging (DOI) data has seen significant developments over the last few years. When compared to fMRI, signals originating from optical imaging are tainted by more physiology and the separation of activation from this background can be difficult in some cases. In this work, we show that the use of time-frequency techniques based on wavelets distinguish different physiological sources from the evoked response to a given stimulus. In particular, we show that analytical complex wavelets identify synchronies in the signal at different scales. These synchronies are then used to extract activation information from the DOI data in order to estimate the evoked hemodynamic response or to define a new type of contrast between two conditions. This work presents both simulations and applications with real data (visual stimulation and motor tasks experiments).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.946

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 head0.035
GPT teacher head0.326
Teacher spread0.291 · 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