alexrockhill

import os.path as op
import numpy as np
import mne
import mne_gui_addons
import autoreject
from mne.time_frequency import csd_tfr

fs_dir = mne.datasets.fetch_fsaverage(verbose=True)
subjects_dir = op.dirname(fs_dir)

alexrockhill

# -*- coding: utf-8 -*-
"""
.. _ex-inverse-dics-epochs:
=======================================================================
Compute source level time-frequency timecourses using a DICS beamformer
=======================================================================
In this example, a Dynamic Imaging of Coherent Sources (DICS)
:footcite:`GrossEtAl2001` beamformer is used to transform sensor-level
time-frequency objects to the source level. We will look at the event-related
synchronization (ERS) of beta band activity in the :ref:`somato dataset

alexrockhill

import os
import os.path as op
import numpy as np
import mne
import imageio

misc_path = mne.datasets.misc.data_path()
sample_path = mne.datasets.sample.data_path()
subjects_dir = sample_path / 'subjects'
subject = 'fsaverage'

alexrockhill

import os
import os.path as op
import numpy as np
import mne
import imageio

misc_path = mne.datasets.misc.data_path()
sample_path = mne.datasets.sample.data_path()
subjects_dir = misc_path / 'seeg'
subject = 'sample_seeg'

alexrockhill

For my GSoC this year, I made a graphical user interface (GUI) to view estimates of the time-course of activity in the brain from non-invasive electroecephalography (EEG) and magnetoencephalography (MEG) recordings. Although, the main pull request (PR) is ready but hasn't yet been merged (mne-tools/mne-python#10920), I am very happy with the end result and will see it through to the finish in the very near future. Being my second GSoC, I took on a project that was more central to the function of the MNE-Python tool; viewing estimates of brain activity, than my previous GSoC, which dealt with the new extension of supporting intracranial electrophysiology. I hope this will be an upgrade in functionality that empowers many users because of its centrality to MNE-Python. I really tried to make the development very stable and a good example because of this importance as well and I'm really happy with that although there are more PRs left on that (below).

The first task I took on that was i

alexrockhill

My GSoC project to make a graphical user interface (GUI) to locate the positions of electrical recording contacts inside a patient's head who has been implanted with intracranial electrodes was a huge success.

I started by developing the accompanying routines to prepare the input data and process the data once the GUI has been used first. This process is described here:

https://mne.tools/dev/auto_tutorials/clinical/10_ieeg_localize.html

The pull requests (PRs) that contributed to this development are here:

mne-tools/mne-python#9484 mne-tools/mne-python#9544

alexrockhill

"""
.. _tut_working_with_ecog:

======================
Working with ECoG data
======================

MNE supports working with more than just MEG and EEG data. Here we show some
of the functions that can be used to facilitate working with
electrocorticography (ECoG) data.

alexrockhill

# -*- coding: utf-8 -*-
"""Test the MNE BIDS converter.

For each supported file format, implement a test.
"""
# Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
#          Teon L Brooks <teon.brooks@gmail.com>
#          Chris Holdgraf <choldgraf@berkeley.edu>
#          Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#          Matt Sanderson <matt.sanderson@mq.edu.au>

alexrockhill

from fbs_runtime.application_context.PyQt5 import ApplicationContext
from PyQt5.QtWidgets import (QMainWindow, QLabel, QPushButton,
                             QWidget, QStackedWidget)
import sys
import os
import os.path as op


class AppContext(ApplicationContext):
    def run(self, path):

alexrockhill

def write_anat(bids_root, subject, t1w, session=None, acquisition=None,
               raw=None, trans=None, deface=False, overwrite=False,
               verbose=False):
    """Put anatomical MRI data into a BIDS format.

    Given a BIDS directory and a T1 weighted MRI scan for a certain subject,
    format the MRI scan to be in BIDS format and put it into the correct
    location in the bids_dir. If a transformation matrix is supplied, a
    sidecar JSON file will be written for the T1 weighted data.