Artifact Detection and BCI-Fit Artifact Report #336
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Add channels back from devices, search for frontal channels to do eye blink detection from.
lawhead
approved these changes
Aug 9, 2024
| percent_bad: float = 50.0, | ||
| detect_eog: bool = True, | ||
| eye_channels: Optional[List[str]] = None, | ||
| detect_voltage: bool = True) -> None: |
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Rather than having a of boolean flag for each artifact type it might be more flexible here to have a single parameter that is a list of ArtifactTypes of interest.
| extra_labels: Optional[Annotations] = None) -> Annotations: | ||
| """Label the artifacts in the raw data.""" | ||
| # Create an empty annotations object to store all the annotations | ||
| annotations = mne.Annotations(0, 0, 'start') |
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This class is very large and it might make sense to split up its responsibilities. One option is to introduce the concept of an ArtifactLabeler with a subclass for each type (ex. VoltageLabeler, EOGEventLabeler). Then this method could iterate through the ArtifactTypes of interest, matching each one to a labeler, and combine the results.
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Overview
Added an Artifact Detection class to the
signal.evaluatemodule. Updated the artifact report section to include information about artifacts detected.Ticket
https://www.pivotaltracker.com/story/show/187526878
https://www.pivotaltracker.com/story/show/187833646
Contributions
artifact.py: module added to detect and report on artifacts in EEG data. This replaces the old evaluator and rules modules.report.py: updated the artifact report to extract relevant information and add to the PDF report.Test
make test-all& using thedemo_report.pyto produce reports.Documentation
Changelog