alexrockhill

from mne import create_info
from mne.io import Raw,RawArray
import numpy as np

for i in range(100):
    raw_info = create_info([str(i) for i in range(100)],25000,['eeg' for _ in range(100)])
    raw = RawArray(np.ones((100,int(1e7))),raw_info)
    raw.save('test%i-raw.fif' %(i))

raw = Raw('test0-raw.fif',preload='test_workfile')

alexrockhill

<?xml version="1.0"?>
<BrainVisionCapTrakFileV1 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema">
  <CapTrakFileVersion>1.10</CapTrakFileVersion>
  <RecordingDateTime>2018.01.12 10:48:44</RecordingDateTime>
  <CapSize>58</CapSize>
  <HeadShapeName>Round</HeadShapeName>
  <CapTrakElectrodeList>
    <CapTrakElectrode>
      <Name>LPA</Name>
      <X>-83.887782650513117</X>

alexrockhill

import inspect
import os.path as op
import shutil

import numpy as np
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
                           assert_allclose, assert_equal)
import pytest

from mne.utils import _TempDir, run_tests_if_main

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.

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

# -*- 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

"""
.. _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

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

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

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'