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

.
study_name = "localizer"
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")]

# Noise estimation
noise_cov = "emptyroom"