Early History of Electroencephalography and Establishment of the American Clinical Neurophysiology Society
Bibliographic record
Abstract
The field of electroencephalography (EEG) had its origin with the discovery of recordable electrical potentials from activated nerves and muscles of animals and in the last quarter of the 19th century from the cerebral cortex of animals. By the 1920s, Hans Berger, a neuropsychiatrist from Germany, recorded potentials from the scalp of patients with skull defects and, a few years later, with more sensitive equipment from intact subjects. Concurrently, the introduction of electronic vacuum tube amplification and the cathode ray oscilloscope was made by American physiologists or "axonologists," interested in peripheral nerve recordings. Berger's findings were independently confirmed in early 1934 by Lord Adrian in England and by Hallowell Davis at Harvard, in the United States. In the United States, the earliest contributions to human EEG were made by Hallowell Davis, Herbert H. Jasper, Frederic A. Gibbs, William Lennox, and Alfred L. Loomis. Remarkable progress in the development of EEG as a useful clinical tool followed the 1935 report by the Harvard group on the electrographic and clinical correlations in patients with absence (petit mal) seizures and altered states of consciousness. Technical aspects of the EEG and additional clinical EEG correlations were elucidated by the above investigators and a number of others. Further study led to gatherings of the EEG pioneers at Loomis' laboratory in New York (1935-1939), Regional EEG society formation, and the American Clinical Neurophysiology Society in 1946.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".