Funloc data¶
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 | ❌ |
Dataset source¶
This dataset was acquired from https://osf.io/upj3h/download?version=1
Configuration¶
Click to expand
Python
.
from pathlib import Path
data_root = Path("~/mne_data").expanduser().resolve()
bids_root = data_root / "MNE-funloc-data"
deriv_root = data_root / "derivatives" / "mne-bids-pipeline" / "MNE-funloc-data"
subjects_dir = bids_root / "derivatives" / "freesurfer" / "subjects"
task = "funloc"
ch_types = ["meg", "eeg"]
data_type = "meg"
# filter
l_freq = None
h_freq = 50.0
# maxfilter
use_maxwell_filter: bool = True
crop_runs = (40, 190)
mf_st_duration = 60.0
# SSP
spatial_filter = "ssp"
ssp_ecg_channel = {"sub-01": "MEG0111", "sub-02": "MEG0141"}
n_proj_eog = dict(n_mag=1, n_grad=1, n_eeg=2)
n_proj_ecg = dict(n_mag=1, n_grad=1, n_eeg=0)
# Epochs
epochs_tmin = -0.2
epochs_tmax = 0.5
epochs_decim = 5 # 1000 -> 200 Hz
baseline = (None, 0)
conditions = [
"auditory/standard",
# "auditory/deviant",
"visual/standard",
# "visual/deviant",
]
decode = False
decoding_time_generalization = False
# contrasts
# contrasts = [("auditory", "visual")]