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Record W2811312676 · doi:10.1088/1361-6560/aad0c2

Scalp sensor for simultaneous acoustic emission detection and electroencephalography during transcranial ultrasound

2018· article· en· W2811312676 on OpenAlexaff
Spencer T. Brinker, Calum Crake, John R. Ives, Ellen J. Bubrick, Nathan McDannold

Bibliographic record

VenuePhysics in Medicine and Biology · 2018
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsWestern University
FundersNational Cancer InstituteNational Institutes of Health
KeywordsElectroencephalographyTranscranial DopplerUltrasoundBiomedical engineeringScalpImaging phantomSIGNAL (programming language)Computer scienceUltrasound energyMaterials scienceAcousticsMedicineRadiologySurgeryPhysics

Abstract

fetched live from OpenAlex

Focused ultrasound is now capable of noninvasively penetrating the intact human skull and delivering energy to specific areas of the brain with millimeter accuracy. The ultrasound energy is supplied in high-intensities to create brain lesions or at low-intensities to produce reversible physiological interventions. Conducting acoustic emission detection (AED) and electroencephalography (EEG) during transcranial focused ultrasound may lead to several new brain treatment and research applications. This study investigates the feasibility of using a novel scalp senor for acquiring concurrent AED and EEG during clinical transcranial ultrasound. A piezoelectric disk is embedded in a plastic cup EEG electrode to form the sensor. The sensor is coupled to the head via an adhesive/conductive gel-dot. Components of the sensor prototype are tested for AED and EEG signal quality in a bench top investigation with a functional ex vivo skull phantom.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.299
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations7
Published2018
Admission routes1
Has abstractyes

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