hMT+ Localizer¶
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/ds003392
How to download this dataset
Run in your terminal:
Run in your terminal
openneuro-py download \
--dataset=ds003392 \
--include=sub-01 \
--include=sub-emptyroom/ses-19111211
Configuration¶
Click to expand
Python
.
bids_root = "~/mne_data/ds003392"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds003392"
subjects = ["01"]
task = "localizer"
# usually a good idea to use True, but we know no bads are detected for this dataset
find_flat_channels_meg = False
find_noisy_channels_meg = False
use_maxwell_filter = True
ch_types = ["meg"]
l_freq = 1.0
h_freq = 40.0
raw_resample_sfreq = 250
crop_runs = (0, 180)
# Artifact correction.
spatial_filter = "ica"
ica_algorithm = "picard-extended_infomax"
ica_max_iterations = 1000
ica_l_freq = 1.0
ica_n_components = 0.99
# Epochs
epochs_tmin = -0.2
epochs_tmax = 1.0
baseline = (None, 0)
# Conditions / events to consider when epoching
conditions = ["coherent", "incoherent"]
# Decoding
decode = True
decoding_time_generalization = True
decoding_time_generalization_decim = 4
contrasts = [("incoherent", "coherent")]
decoding_csp = True
decoding_csp_times = []
decoding_csp_freqs = {
"alpha": (8, 12),
}
# Noise estimation
noise_cov = "emptyroom"