Using electroencephalography as a tool to understand auditory perception: Event-related and time-frequency analyses
Bibliographic record
Abstract
Electroencephalography (EEG) largely reflects postsynaptic field potentials summed over many (hundreds of thousands of) neurons that are aligned in time and orientation. These electrical fields propagate in all directions such that determination of the sources of electrical fields measured at the surface of the head is much less accurate than localizations using fMRI. However, EEG can be measured with sub-millisecond timing resolution, offering a great advantage for studies of hearing. EEG can measure activity from various nuclei along the subcortical pathway, from primary and secondary auditory cortex and from cortical regions beyond. EEG can be particularly useful for understanding preconscious processing stages, and auditory processing in infants and others who can not make verbal responses. Traditional methods of analysis relate peaks (“components”) in the EEG time waveform to stages of processing. However, communication between brain circuits is reflected in neural oscillations, which can be measured through time-frequency analyses of EEG recordings. Such approaches reveal, for example, how frequency is encoded in the brainstem, and how predictive timing and predictive coding are accomplished in the cortex. I will illustrate these points with example applications largely from our lab and argue that EEG can greatly enhance our interpretation of psychophysical data.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".