Real‐time display of artifact‐free electroencephalography during functional magnetic resonance imaging and magnetic resonance spectroscopy in an animal model of epilepsy
Bibliographic record
Abstract
Simultaneous recording of electroencephalogram (EEG) and functional MRI (fMRI) or MR spectroscopy (MRS) can provide further insight into our understanding of the underlying mechanisms of neurologic disorders. Current technology for simultaneous EEG and MRI recording is limited by extensive postacquisition processing of the data. Real-time display of artifact-free EEG recording during fMRI/MRS studies is essential in studies that involve epilepsy to ensure that they address specific EEG features such as epileptic spikes or seizures. By optimizing the EEG recording equipment to maximize the common mode rejection ratio of its amplifiers, a unique EEG system was designed and tested that allowed real-time display of the artifact-free EEG during fMRI/MRS in an animal model of epilepsy. Spike recordings were optimized by suppression of the background EEG activity using fast-acting and easily controlled inhalational anesthesia. Artifact suppression efficiency of 70-100% was achieved following direct subtraction of referentially recorded filtered EEG tracings from active electrodes, which were located in close proximity to each other (over homologous occipital cortices) and a reference electrode. Two independent postacquisition processing tools, independent component analysis and direct subtraction of unfiltered digital EEG data in MATLAB, were used to verify the accuracy of real-time EEG display.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".