MNE toolbox#

MNE is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, NIRS, and more.

Warning#

Be careful with assignments in python… image

MNE Report#

The mne.Report functions are great to generate HTML with a summary of your data, plots and even code chunks. The document is interactive, you can choose to hide or expand some images, and some of the figures (e.g., topographies with slider) are interactive.
Documentation: https://mne.tools/dev/auto_tutorials/intro/70_report.html#sphx-glr-auto-tutorials-intro-70-report-py

Example of a single-subject report#

In this example we have epoched data (mne epoched object saved in .fif files). One file per subject, containing the epochs for the different conditions. We want to make a report html file for each subject containing:

  • A figure with Event description (event labels in time)

  • Number of epochs for each condition

  • Visualizations of the evoked data (average of conditions):

    • ERP waveforms and global field power

    • ERP images (x-time , y-epoch, colormap-amplitude)

    • Topographies with interactive slider

    • Time frequency power

Read data#

epochs = mne.read_epochs(glob(dirinput + 's04_DiN_epoched_ICrem.fif')[0])  # glob searches file name pattern in dirinput

Initialize the report#

 report = mne.Report(title='My example report')
  # Add epochs will automatically add an ERP image but this will average across all conditions 
  report.add_epochs(epochs=epochs, title='Epochs', psd=False)  #  PSD plot can be turned on

Create evoked objects#

  #To get the ERPs per condition we need to compute the averages

Add elements to the report#

  (...)

Write to .html file#

Quality assesment#

Multivariate pattern analysis#