KIT phantom data.¶
https://mne.tools/dev/documentation/datasets.html#kit-phantom-dataset
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://mne.tools/dev/generated/mne.datasets.phantom_kit.data_path.html
Configuration¶
Click to expand
Python
bids_root = "~/mne_data/MNE-phantom-KIT-data"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/MNE-phantom-KIT-data"
task = "phantom"
ch_types = ["meg"]
# Preprocessing
l_freq = None
h_freq = 40.0
regress_artifact = dict(
picks="meg", picks_artifact=["MISC 001", "MISC 002", "MISC 003"]
)
# Epochs
epochs_tmin = -0.08
epochs_tmax = 0.18
epochs_decim = 10 # 2000->200 Hz
baseline = (None, 0)
conditions = ["dip01", "dip13", "dip25", "dip37", "dip49"]
# Decoding
decode = True # should be very good performance