MétaCan
Menu
← Back to cohort
Record W2959288997 · doi:10.1101/700054

Chirp analyzer for estimating amplitude and latency of steady-state auditory envelope following responses

2019· preprint· en· W2959288997 on OpenAlexaff
Yalina García‐Puente, Matías Zañartu, Pavel Prado

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsShort-time Fourier transformFourier transformAmplitudeChirpAcousticsAmplitude modulationSpeech recognitionSpectrum analyzerComputer scienceInstantaneous phaseMathematicsFrequency modulationPhysicsFourier analysisTelecommunicationsBandwidth (computing)OpticsMathematical analysisRadar

Abstract

fetched live from OpenAlex

Abstract The envelope following response (EFR) is a scalp-recorded evoked potential elicited by carrier tones or noise, modulated in amplitude with a continuous sweep of modulation frequencies. This non-stationary response reflects the phase-locked neural activity of the auditory pathway to the temporal envelope of sounds and has been commonly assessed by fixed-frequency methods based on the discrete Fourier transform, such as the Fourier Analyzer (FA). In this work, we study the estimation of the EFR with the use of explicit time-frequency methods, which offer more information about the energy distribution of the recorded signal, such as the Short-Term Fourier Transform (STFT) and the Morlet Continuous Wavelet Transform (CWT). We further introduce the Chirp Analyzer (CA), which is similar to FA, but using as basis function the same linear chirp that amplitude-modulates the carrier stimulus. In a direct comparison using controlled simulated responses, the CA showed to be able to estimate the correct EFR amplitudes, without the typical bias offered by the estimation using STFT (equivalent to FA) and more robust to noise than the CWT method, although with higher sensitivity to the presence of a delay in the response with respect to the stimulus. For addressing the latter issue, we also propose here a novel methodology for estimating the apparent latency of the response. This method proved to be reliable when using the STFT and the CA methods, as assessed using simulated responses. The estimation of the EFR amplitude with any of the methods, but especially with CA, should be corrected by using the estimated delay when possible. An illustrative application of these methods to small datasets of a rat and a human newborn, suggested that all time-frequency methods can be used to study the EFR amplitudes in a wide range of modulation frequencies, but they should be interpreted in the light of the limitations shown in the simulation studies.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.253
Teacher spread0.228 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicNeural dynamics and brain function→French-language works237,207→