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
Loading, please wait
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
010121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121

1 rows × 51 columns

Global field power
Global field power
Global field power
Global field power
Global field power
  """
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