phantom | ||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
trial_type | subject | run | dip01 | dip02 | dip03 | dip04 | dip05 | dip06 | dip07 | dip08 | dip09 | dip10 | dip11 | dip12 | dip13 | dip14 | dip15 | dip16 | dip17 | dip18 | dip19 | dip20 | dip21 | dip22 | dip23 | dip24 | dip25 | dip26 | dip27 | dip28 | dip29 | dip30 | dip31 | dip32 | dip33 | dip34 | dip35 | dip36 | dip37 | dip38 | dip39 | dip40 | dip41 | dip42 | dip43 | dip44 | dip45 | dip46 | dip47 | dip48 | dip49 |
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phantom | ||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
trial_type | subject | run | dip01 | dip02 | dip03 | dip04 | dip05 | dip06 | dip07 | dip08 | dip09 | dip10 | dip11 | dip12 | dip13 | dip14 | dip15 | dip16 | dip17 | dip18 | dip19 | dip20 | dip21 | dip22 | dip23 | dip24 | dip25 | dip26 | dip27 | dip28 | dip29 | dip30 | dip31 | dip32 | dip33 | dip34 | dip35 | dip36 | dip37 | dip38 | dip39 | dip40 | dip41 | dip42 | dip43 | dip44 | dip45 | dip46 | dip47 | dip48 | dip49 | |
01 | 01 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 | 21 |
1 rows × 51 columns
"""
KIT phantom data.
https://mne.tools/dev/documentation/datasets.html#kit-phantom-dataset
"""
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
Platform Linux-5.15.0-1053-aws-x86_64-with-glibc2.35
Python 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]
Executable /home/circleci/python_env/bin/python3.10
CPU x86_64 (36 cores)
Memory 68.6 GB
Core
├☑ mne 1.7.0.dev156+g415e7f68e (devel, latest release is 1.6.1)
├☑ numpy 1.26.4 (OpenBLAS 0.3.23.dev with 2 threads)
├☑ scipy 1.12.0
└☑ matplotlib 3.8.3 (backend=agg)
Numerical (optional)
├☑ sklearn 1.4.1.post1
├☑ numba 0.59.1
├☑ nibabel 5.2.1
├☑ pandas 2.2.1
└☐ unavailable nilearn, dipy, openmeeg, cupy
Visualization (optional)
├☑ pyvista 0.43.4 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.2 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt 0.11.0
├☑ vtk 9.3.0
├☑ qtpy 2.4.1 (PyQt6=6.6.0)
└☐ unavailable ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify
Ecosystem (optional)
├☑ mne-bids 0.15.0.dev43+g17d20c132
├☑ mne-bids-pipeline 1.8.0
└☐ unavailable mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo