The role of electroencephalography in epilepsy research—From seizures to interictal activity and comorbidities
Bibliographic record
Abstract
Electroencephalography (EEG) has been instrumental in epilepsy research for the past century, both for basic and translational studies. Its contributions have advanced our understanding of epilepsy, shedding light on the pathophysiology and functional organization of epileptic networks, and the mechanisms underlying seizures. Here we re-examine the historical significance, ongoing relevance, and future trajectories of EEG in epilepsy research. We describe traditional approaches to record brain electrical activity and discuss novel cutting-edge, large-scale techniques using micro-electrode arrays. Contemporary EEG studies explore brain potentials beyond the traditional Berger frequencies to uncover underexplored mechanisms operating at ultra-slow and high frequencies, which have proven valuable in understanding the principles of ictogenesis, epileptogenesis, and endogenous epileptogenicity. Integrating EEG with modern techniques such as optogenetics, chemogenetics, and imaging provides a more comprehensive understanding of epilepsy. EEG has become an integral element in a powerful suite of tools for capturing epileptic network dynamics across various temporal and spatial scales, ranging from rapid pathological synchronization to the long-term processes of epileptogenesis or seizure cycles. Advancements in EEG recording techniques parallel the application of sophisticated mathematical analyses and algorithms, significantly augmenting the information yield of EEG recordings. Beyond seizures and interictal activity, EEG has been instrumental in elucidating the mechanisms underlying epilepsy-related cognitive deficits and other comorbidities. Although EEG remains a cornerstone in epilepsy research, persistent challenges such as limited spatial resolution, artifacts, and the difficulty of long-term recording highlight the ongoing need for refinement. Despite these challenges, EEG continues to be a fundamental research tool, playing a central role in unraveling disease mechanisms and drug discovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".