OMEGA Resting State Sample 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://openneuro.org/datasets/ds000247
How to download this dataset
Run in your terminal:
Run in your terminal
openneuro-py download \
--dataset=ds000247 \
--include=sub-0002/ses-01/meg
Configuration¶
Click to expand
Python
.
import numpy as np
bids_root = "~/mne_data/ds000247"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds000247"
subjects = ["0002"]
sessions = ["01"]
task = "rest"
task_is_rest = True
crop_runs = (0, 100) # to speed up computations
ch_types = ["meg"]
spatial_filter = "ssp"
l_freq = 1.0
h_freq = 40.0
rest_epochs_duration = 10
rest_epochs_overlap = 0
epochs_tmin = 0
baseline = None
time_frequency_conditions = ["rest"]
time_frequency_freq_min = 1.0
time_frequency_freq_max = 30.0
time_frequency_cycles = np.arange(time_frequency_freq_min, time_frequency_freq_max) / 4
time_frequency_subtract_evoked = True