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

General
Filename(s) sub-01_task-phantom_ave.fif
MNE object type Evoked
Measurement date 2023-08-31 at 04:32:40 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: dip01
Time range -0.080 – 0.180 s
Baseline -0.080 – 0.000 s
Sampling frequency 200.00 Hz
Time points 53
Channels
Magnetometers
Head & sensor digitization Not available
Filters
Highpass 0.03 Hz
Lowpass 40.00 Hz
Global field power
General
Filename(s) sub-01_task-phantom_ave.fif
MNE object type Evoked
Measurement date 2023-08-31 at 04:32:40 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: dip13
Time range -0.080 – 0.180 s
Baseline -0.080 – 0.000 s
Sampling frequency 200.00 Hz
Time points 53
Channels
Magnetometers
Head & sensor digitization Not available
Filters
Highpass 0.03 Hz
Lowpass 40.00 Hz
Global field power
General
Filename(s) sub-01_task-phantom_ave.fif
MNE object type Evoked
Measurement date 2023-08-31 at 04:32:40 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: dip25
Time range -0.080 – 0.180 s
Baseline -0.080 – 0.000 s
Sampling frequency 200.00 Hz
Time points 53
Channels
Magnetometers
Head & sensor digitization Not available
Filters
Highpass 0.03 Hz
Lowpass 40.00 Hz
Global field power
General
Filename(s) sub-01_task-phantom_ave.fif
MNE object type Evoked
Measurement date 2023-08-31 at 04:32:40 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: dip37
Time range -0.080 – 0.180 s
Baseline -0.080 – 0.000 s
Sampling frequency 200.00 Hz
Time points 53
Channels
Magnetometers
Head & sensor digitization Not available
Filters
Highpass 0.03 Hz
Lowpass 40.00 Hz
Global field power
General
Filename(s) sub-01_task-phantom_ave.fif
MNE object type Evoked
Measurement date 2023-08-31 at 04:32:40 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: dip49
Time range -0.080 – 0.180 s
Baseline -0.080 – 0.000 s
Sampling frequency 200.00 Hz
Time points 53
Channels
Magnetometers
Head & sensor digitization Not available
Filters
Highpass 0.03 Hz
Lowpass 40.00 Hz
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-5.15.0-1057-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               1.8.0.dev79+gcaba81b9f (devel, latest release is 1.7.1)
├☑ numpy             2.0.0 (OpenBLAS 0.3.27 with 2 threads)
├☑ scipy             1.13.1
└☑ matplotlib        3.9.0 (backend=agg)

Numerical (optional)
├☑ sklearn           1.5.0
├☑ numba             0.60.0
├☑ nibabel           5.2.1
├☑ pandas            2.2.2
├☑ h5io              0.2.3
├☑ h5py              3.11.0
└☐ unavailable       nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.43.10 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.2 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt         0.11.1
├☑ vtk               9.3.0
├☑ qtpy              2.4.1 (PyQt6=6.7.1)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.16.0.dev3+g0e3cbc66a
├☑ mne-bids-pipeline 1.9.0
├☑ edfio             0.4.2
├☑ pybv              0.7.5
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, eeglabio, mffpy