attentionalblink | |||
---|---|---|---|
session | subject | cathodalpre | |
01 | 1416 |
1 rows × 2 columns
General | ||
---|---|---|
Filename(s) | sub-01_ses-cathodalpre_task-attentionalblink_ave.fif | |
MNE object type | Evoked | |
Measurement date | Unknown | |
Participant | sub-01 | |
Experimenter | Unknown | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: 61450 | |
Time range | -0.199 – 0.500 s | |
Baseline | -0.199 – 0.000 s | |
Sampling frequency | 512.00 Hz | |
Time points | 359 | |
Channels | ||
EEG | ||
Head & sensor digitization | 67 points | |
Filters | ||
Highpass | 0.30 Hz | |
Lowpass | 40.00 Hz | |
Projections | Average EEG reference (on) |
General | ||
---|---|---|
Filename(s) | sub-01_ses-cathodalpre_task-attentionalblink_ave.fif | |
MNE object type | Evoked | |
Measurement date | Unknown | |
Participant | sub-01 | |
Experimenter | Unknown | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: 61511 | |
Time range | -0.199 – 0.500 s | |
Baseline | -0.199 – 0.000 s | |
Sampling frequency | 512.00 Hz | |
Time points | 359 | |
Channels | ||
EEG | ||
Head & sensor digitization | 67 points | |
Filters | ||
Highpass | 0.30 Hz | |
Lowpass | 40.00 Hz | |
Projections | Average EEG reference (on) |
General | ||
---|---|---|
Filename(s) | sub-01_ses-cathodalpre_task-attentionalblink_ave.fif | |
MNE object type | Evoked | |
Measurement date | Unknown | |
Participant | sub-01 | |
Experimenter | Unknown | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: 61450 - 61511 | |
Time range | -0.199 – 0.500 s | |
Baseline | -0.199 – 0.000 s | |
Sampling frequency | 512.00 Hz | |
Time points | 359 | |
Channels | ||
EEG | ||
Head & sensor digitization | 67 points | |
Filters | ||
Highpass | 0.30 Hz | |
Lowpass | 40.00 Hz | |
Projections | Average EEG reference (on) |
"""tDCS EEG."""
bids_root = "~/mne_data/ds001810"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds001810"
task = "attentionalblink"
interactive = False
ch_types = ["eeg"]
eeg_template_montage = "biosemi64"
reject = dict(eeg=100e-6)
baseline = (None, 0)
conditions = ["61450", "61511"]
contrasts = [("61450", "61511")]
decode = True
decoding_n_splits = 3 # only for testing, use 5 otherwise
l_freq = 0.3
subjects = ["01"]
sessions = "all"
interpolate_bads_grand_average = False
n_jobs = 4
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 1 thread)
├☑ 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
├☑ eeglabio 0.0.2-4
├☑ edfio 0.4.2
├☑ pybv 0.7.5
└☐ unavailable mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, mffpy