Applications of Electroencephalography to Characterize Brain Activity
Bibliographic record
Abstract
A wide array of neuroimaging technologies are now available that offer unprecedented opportunities to study the brain in health and disease. Each technology has associated strengths and weaknesses that need to be considered to maximize their utility, especially when used in combination. One imaging technology, electroencephalography (EEG), has been in use for more than 80 years, but as a result of recent technologic advancements EEG has received renewed interest as an inexpensive, noninvasive and versatile technique to evaluate neural activity in the brain. In part, this is due to new opportunities to combine EEG not only with other imaging modalities, but also with neurostimulation and robotics technologies. When used in combination, noninvasive brain stimulation and EEG can be used to study cause-and-effect relationships between interconnected brain regions providing new avenues to study brain function. Although many of these approaches are still in the developmental phase, there is substantial promise in their ability to deepen our understanding of brain function. The ability to capture the causal relationships between brain function and behavior in individuals with neurologic disorders or injury has important clinical implications for the development of novel biomarkers of recovery and response to therapeutic interventions. The goals of this paper are to provide an overview of the fundamental principles of EEG; discuss past, present, and future applications of EEG in the clinical management of stroke; and introduce the technique of combining EEG with a form of noninvasive brain stimulation, transcranial magnetic stimulation, as a powerful synergistic research paradigm to characterize brain function in both health and disease.Video Abstract available (see Supplemental Digital Content 1, http://links.lww.com/JNPT/A87) for more insights from the authors.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".