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Brainstorm - Auditory Dataset.

See https://openneuro.org/datasets/ds000246/versions/1.0.0 for more information.

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/ds000246

How to download this dataset

Run in your terminal:

Run in your terminal
openneuro-py download \
             --dataset=ds000246 \
             --include=sub-0001/meg/sub-0001_task-AEF_run-01_meg.ds \
             --include=sub-0001/meg/sub-0001_task-AEF_run-01_meg.json \
             --include=sub-0001/meg/sub-0001_task-AEF_run-01_channels.tsv

Configuration

Click to expand
Python
bids_root = "~/mne_data/ds000246"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds000246"

runs = ["01"]
crop_runs = (0, 120)  # Reduce memory usage on CI system
read_raw_bids_verbose = "error"  # No BIDS -> MNE mapping found for channel ...
l_freq = 0.3
h_freq = 100
epochs_decim = 4
subjects = ["0001"]
ch_types = ["meg"]
reject = dict(mag=4e-12, eog=250e-6)
conditions = ["standard", "deviant", "button"]
epochs_metadata_tmin = ["standard", "deviant"]  # for testing only
contrasts = [("deviant", "standard")]
decode = True
decoding_time_generalization = True
decoding_time_generalization_decim = 4
on_error = "abort"
plot_psd_for_runs = []  # too much memory on CIs

parallel_backend = "dask"
dask_worker_memory_limit = "2G"
dask_temp_dir = "./.dask-worker-space"
dask_open_dashboard = True
n_jobs = 2

Generated output

Summary reports

sub-0001_task-AEF_report.html

sub-average_task-AEF_report.html