matchingpennies
trial_type subject raised-left/match-false raised-left/match-true raised-right/match-false raised-right/match-true
05 75 104 55 66
06 79 62 59 100
07 50 100 49 101
08 70 55 66 109
09 62 91 86 61
10 65 76 52 107
11 72 82 60 86

7 rows × 5 columns

General
Filename(s) sub-05_task-matchingpennies_ave.fif
MNE object type Evoked
Measurement date Unknown
Participant sub-05
Experimenter Unknown
Acquisition
Aggregation average of 1 epochs
Condition Grand average: 0.42 × raised-left/match-false + 0.58 × raised-left/match-true
Time range -0.200 – 0.500 s
Baseline -0.200 – 0.000 s
Sampling frequency 5000.00 Hz
Time points 3,501
Channels
EEG
Head & sensor digitization Not available
Filters
Highpass 0.00 Hz
Lowpass 100.00 Hz
Projections Average EEG reference (on)
Global field power
General
Filename(s) sub-05_task-matchingpennies_ave.fif
MNE object type Evoked
Measurement date Unknown
Participant sub-05
Experimenter Unknown
Acquisition
Aggregation average of 1 epochs
Condition Grand average: 0.45 × raised-right/match-false + 0.55 × raised-right/match-true
Time range -0.200 – 0.500 s
Baseline -0.200 – 0.000 s
Sampling frequency 5000.00 Hz
Time points 3,501
Channels
EEG
Head & sensor digitization Not available
Filters
Highpass 0.00 Hz
Lowpass 100.00 Hz
Projections Average EEG reference (on)
Global field power
General
Filename(s) sub-05_task-matchingpennies_ave.fif
MNE object type Evoked
Measurement date Unknown
Participant sub-05
Experimenter Unknown
Acquisition
Aggregation average of 1 epochs
Condition Grand average: (0.42 × raised-left/match-false + 0.58 × raised-left/match-true) - (0.45 × raised-right/match-false + 0.55 × raised-right/match-true)
Time range -0.200 – 0.500 s
Baseline -0.200 – 0.000 s
Sampling frequency 5000.00 Hz
Time points 3,501
Channels
EEG
Head & sensor digitization Not available
Filters
Highpass 0.00 Hz
Lowpass 100.00 Hz
Projections Average EEG reference (on)
Global field power
Full-epochs decoding
Based on N=1 subjects. Each dot represents the mean cross-validation score for a single subject. The dashed line is expected chance performance.
  """Matchingpennies EEG experiment."""

bids_root = "~/mne_data/eeg_matchingpennies"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/eeg_matchingpennies"

subjects = ["05"]
task = "matchingpennies"
ch_types = ["eeg"]
interactive = False
reject = {"eeg": 150e-6}
conditions = ["raised-left", "raised-right"]
contrasts = [("raised-left", "raised-right")]
decode = True

interpolate_bads_grand_average = False

l_freq = None
h_freq = 100
zapline_fline = 50
zapline_iter = False


  Platform             Linux-5.15.0-1077-aws-x86_64-with-glibc2.35
Python               3.12.4 (main, Jun  8 2024, 23:40:19) [GCC 11.4.0]
Executable           /home/circleci/.pyenv/versions/3.12.4/bin/python3.12
CPU                  Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz (36 cores)
Memory               68.6 GiB

Core
├☑ mne               1.10.0.dev67+gbe27cf8dd (devel, latest release is 1.9.0)
├☑ numpy             2.1.3 (OpenBLAS 0.3.27 with 2 threads)
├☑ scipy             1.15.2
└☑ matplotlib        3.10.1 (backend=agg)

Numerical (optional)
├☑ sklearn           1.6.1
├☑ numba             0.61.0
├☑ nibabel           5.3.2
├☑ pandas            2.2.3
├☑ h5io              0.2.4
├☑ h5py              3.13.0
└☐ unavailable       nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.44.2 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.3 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt         0.11.2
├☑ vtk               9.3.1
├☑ qtpy              2.4.3 (PyQt6=6.8.2)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.17.0.dev32+g3a25345
├☑ mne-bids-pipeline 1.10.0.dev68+gb995b51
├☑ edfio             0.4.6
├☑ pybv              0.7.6
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, eeglabio, mffpy