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"]
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