Ballistocardiogram Correction in Simultaneous EEG/ fMRI Recordings: A Comparison of Average Artifact Subtraction and Optimal Basis Set Methods Using Two Popular Software Tools
Bibliographic record
Abstract
Electroencephalography data recorded during functional magnetic resonance imaging acquisition are subject to large cardiac-related artifacts that must be corrected during postprocessing. This study compared two widely used ballistocardiogram (BCG) correction algorithms as implemented in two software programs. Reduction of BCG amplitude, correlation of corrected data with electrocardiogram traces, correlation of independent components with electrocardiogram traces, and event-related potential signal-to-noise ratio from each algorithm were compared. Both algorithms effectively reduced the BCG artifact, with a slight advantage of average artifact subtraction over the optimal basis set method (0.1-2.2%) when the quality of the correction was examined at the individual subject level. This study provides users of these software tools with an important, practical, and previously unavailable comparison of the performance of these two methods.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".