The effects of digital filtering on mismatch negativity in wakefulness and slow‐wave sleep
Bibliographic record
Abstract
The mismatch negativity (MMN) is a response to a deviant auditory stimulus that occurs infrequently in a sequence of otherwise repetitive, homogeneous standard auditory stimuli. The MMN is presumed automatic and independent of conscious awareness. Recording of the MMN during unconscious states may be problematic. The frequency content of the long-lasting MMN may overlap and summate with other event-related slow potentials and low-frequency background electroencephalogram (EEG) activity. The purpose of this study is to determine the optimal filter settings for recording the MMN during unconscious states. Auditory event-related potentials (ERPs) were recorded from eight subjects in an oddball paradigm during wakefulness and Stages 3 and 4 of sleep [slow-wave sleep (SWS)] using a 0.16-35 Hz analogue bandpass. Deviant probability was 0.033. Stimulus-onset asynchrony was 150 ms. The EEG data were subsequently digitally filtered in the frequency domain. The low-pass filter was set at either 24, 12 or 6 Hz, and the high-pass filter at either 1, 2, 3 or 4 Hz. Applying a low-pass filter down to 12 Hz had a minimal impact on the waking or sleeping MMN amplitude. On the other hand, increasing the high-pass setting from 2 to 3 Hz permitted the visualization of the MMN recorded during sleep. The 4 Hz filter showed a similar trend but also markedly attenuated the amplitude of the waking MMN. A high-pass setting of 3 Hz provides a reasonable compromise. It has only a slight effect on the MMN when the subject is conscious, but still attenuates most of the unwanted slow potential activity when the subject enters SWS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".