mne-data: Matchingpennies EEG.¶
See https://github.com/mne-tools/mne-data/releases/tag/matchingpennies-1
Demonstrated features¶
| Feature | This example |
|---|---|
| MEG processing | ❌ |
| EEG processing | ✅ |
| Maxwell filter | ❌ |
| Frequency filter | ✅ |
| Artifact regression | ❌ |
| SSP | ❌ |
| ICA | ❌ |
| Evoked contrasts | ✅ |
| Time-by-time decoding | ✅ |
| Time-generalization decoding | ❌ |
| CSP decoding | ❌ |
| Time-frequency analysis | ❌ |
| BEM surface creation | ❌ |
| Template MRI | ❌ |
| Dask | ❌ |
Dataset source¶
This dataset was acquired from https://github.com/mne-tools/mne-data/releases/download/matchingpennies-1/eeg_matchingpennies.zip
Configuration¶
Click to expand
Python
bids_root = "~/mne_data/eeg_matchingpennies"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/eeg_matchingpennies"
ignore_warnings = [
r"Did not find any eeg\.json associated with sub-",
"HED annotations were detected but could not be parsed",
r"Unable to map the following column\(s\)", # handedness
]
subjects = ["05"]
task = "matchingpennies"
ch_types = ["eeg"]
interactive = False
reject = {"eeg": 150e-6}
conditions = ["raised-left", "raised-right"]
contrasts = [("raised-left", "raised-right")]
decode = True
interpolate_bads_grand_average = False
l_freq = None
h_freq = 100
zapline_fline = 50
# To speed up processing, crop the runs to the first ~10 minutes (rather than all ~31)
crop_runs = (0, 600)