Influence of recording instrumentation on the stimulus artifact tail in the surface acquisition of somatosensory evoked potentials
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".