The Eastern Association of Electroencephalographers: A Canadian/USA success story
Bibliographic record
Abstract
Objective/methods: The Eastern Association of Electroencephalographers (EAEEG), founded in 1946, is recognized as the world's oldest EEG society. This review traces its history, highlighting contributions from notable members and the significance of the Kirshman and Milner lectureships in advancing the field. Results: Although established in Hartford, Connecticut, the society's intellectual roots lie at the Montreal Neurological Institute, home to pioneering EEG researchers Wilder Penfield and Herbert Jasper. Over more than seven decades, the EAEEG has played a pivotal role in fostering research, education, and collaboration across the United States and Canada. Its conferences have featured distinguished keynote lectures, including presentations by three Nobel Laureates, emphasizing the society's prominence in neurophysiological advancements. The society has successfully facilitated transnational collaboration, offering a platform for both trainees and experienced clinicians and scientists to exchange knowledge and promote progress in clinical and basic neurophysiology. Conclusions/Significance: Despite the prominence of large international conferences, the EAEEG's influence underscores the importance of smaller, multinational societies in shaping neurophysiological research and practice. Its history exemplifies how collaborative efforts between the US and Canada can drive scientific innovation and education within a supportive, collegial environment, reinforcing the enduring impact of specialized professional societies on the field.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".