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Influence of recording instrumentation on the stimulus artifact tail in the surface acquisition of somatosensory evoked potentials

2006· article· en· W1987606376 on OpenAlexafffund
N. Hamming, D.F. Lovely

Bibliographic record

VenueMedical Engineering & Physics · 2006
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsUniversity of New Brunswick
FundersScience and Engineering Research BoardNatural Sciences and Engineering Research Council of Canada
KeywordsArtifact (error)Stimulus (psychology)Somatosensory systemInstrumentation (computer programming)Somatosensory evoked potentialComputer scienceNeuroscienceArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Surface recorded somatosensory evoked potentials (SEPs) are neural signals elicited by an external stimulus. In the case of electrically induced SEPs, the artifact generated by the stimulation process can severely distort the signal. The artifact is characterized by a large impulse followed by a slowly decaying tail. In some cases, the artifact tail often lasts well into the initiation of the SEP making the determination of absolute latency very difficult. While the literature often states that the recording instrumentation plays a part in the generation of this artifact tail, no firm evidence has ever been presented. In this work, comparisons are made between three instrumentation systems (BJT, JFET and CMOS) with differing input impedances in an attempt to quantify the effects on the artifact tail. The conclusions from this investigation show that there is no significant interaction between the input impedance of the recording instrumentation and the duration of the artifact tail. Each amplifier type produced results with no significant statistical differences. It was also found that while stimulation amplitude has a weak effect on the artifact tail, the greatest contribution to variation has an inter-subject origin. Consequently, it is concluded that the time constant of the artifact tail must originate from other sources that are subject dependent.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.256

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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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 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

Citations3
Published2006
Admission routes2
Has abstractyes

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