mne-python | MNE : Magnetoencephalography | Dataset library
kandi X-RAY | mne-python Summary
kandi X-RAY | mne-python Summary
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
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Top functions reviewed by kandi - BETA
- Plot evokeds .
- Read measurement information from a FIFF file .
- Get the information about the header .
- Plot volume sources .
- Plot an alignment .
- Get information from a KIT file .
- Read the GDF header .
- Reads the configuration file
- Plot a figure .
- Plot an epoch .
mne-python Key Features
mne-python Examples and Code Snippets
Lines of Code : 45
License : No License
Copylibrary(devtools)
devtools::install_github("mne-tools/mne-r")
library(mne) # load mne and get the mne object
#> Importing MNE version=0.18.dev0, path='/Users/dengeman/github/mne-python/mne'
# the mne object wraps the loaded mne module inside Py For our purpose it is Biosemi128 data format (csv).
ICA artifacts removal consists of two consecutive phases:
1. Extract and plot indepentent components.
*** Decide which of the components to remove ***
2. Remove components.
In this pac triggers = {'condition1': 1, 'condition2': 4,
'condition3': 8, 'condition4': 16}
subject = 'A0001'
exp = MnePreproc(subject, 'MyExp', '/Users/user/Experiments/MyExp', triggers)
exp.load_raw()
exp.filter_raw(low_bound=1)
exp.find_events tmin = 10
tmax = 30
raw.crop(tmin=tmin, tmax=tmax)
IMAGE_INSTALL_append = " python3-pyqt5"
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
canvas = FigureCanvas ( fig )
plot_connectivity_circle ( conmat, all_ch, n_lines=300,
node_angles=node_angles,
import mne
edf = mne.io.read_raw_edf(r"Path name from external drive\PN00\PN00-1.edf")
data = edf.to_data_frame(index="time", time_format="datetime")
data.to_csv("PN00-1Testv3.csv")
import altair as alt
from vega_datasets import data
cars = data.cars()
alt.Chart(cars).mark_point().encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin',
).interactive()
import mne
import matplotlib
matplotlib.use('TkAgg')
def get_plot():
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
window = Tk()
def open_file():
window.filename = filedialog.askopenfilename(initialdir = "Users/fishbacp/Desktop",title
= "Select file",filetypes = (("EDF files","*.edf"),("all files","*.*")))
raw = mn.io.read_raw_edf(windowCommunity Discussions
Trending Discussions on mne-python
QUESTION
My understanding of downsampling is that it is an operation to decrease the sample rate of x by keeping the first sample and then every nth sample after the first. The example provided from the resample method of scipy package clearly illustrated about this operation as depicted the picture which is accessible from the link (https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html) or as extracted below
In an enlarged view, it is evident that the original data points were resampled point by point.
However, using the mne example of downsampling which accessible via the link : https://mne.tools/dev/auto_examples/preprocessing/plot_resample.html , I notice that the data points were not resampled point by point as illustrated visually below
This given that, mne resample is based on the resample method of scipy package as indicated from mne resample function as shown: https://github.com/mne-tools/mne-python/blob/607fb4613fb5a80dd225132a4a53fe43b8fde0fb/mne/filter.py#L1342
May I know whether this issue is due to the ringing artifacts or due to other problems?
Also, are there remedies to mitigate this problem.
Thanks for any insight. Appreciate it
The same question has been asked in mne discussion repo, but un-answered as of the time of writing
...ANSWER
Answered 2020-Jul-24 at 02:09My understanding of downsampling is that it is an operation to decrease the sample rate of x by keeping the first sample and then every nth sample after the first.
Resampling typically consists of two steps: low-pass filtering to avoid aliasing, then sample rate reduction (subselecting samples from the resulting signal). The low-passing actually changes the values, so the subselection-of-filtered-data step will not necessarily yield points that were "on" the original signal.
May I know whether this issue is due to the ringing artifacts or due to other problems?
In this case it's likely due to the (implicit) low-pass filtering in the frequency-domain resampling of the signal. It looks pretty reasonable to me. If you want to play around with it a bit, you can
- Call scipy.signal.resample directly on your data and see how closely it matches.
- Pad your signal, call scipy.signal.resample, and remove the (now reduced-length) padding -- this is what MNE does internally.
- Use scipy.signal.resample_poly directly on your data.
- Manually low-pass filter and then directly subselect samples from the low-passed signal, which is what resample_poly does internally.
Also, scipy.signal.resample does frequency-domain resampling, so implicitly uses a brick-wall filter at Nyquist when downsampling (unless you specify something for the window argument, which gets applied in the frequency domain in addition to the effective brick-wall filter).
p.s. The answer provided is an extract from the discussion with the folk at mne, namely Eric Larson,Brunner Clemens,Phillip Alday. Credit should be given to them
QUESTION
I'm having some difficulty using Mayavi mlab in python with PyCharm IDE from the MNE-Python environment. I access the Conda environment with Mayavi and VTK over a VNC session using a xvnc server from my local MacOS to a Linux cluster machine.
The error I get when opening a mlab window is:
...ANSWER
Answered 2020-Apr-25 at 14:15I am unable to reproduce. Have you made sure that the machine you are accessing has available and loaded up-to-date graphics drivers and/or Mesa software? As an example, I am using TurboVNC to access a remote Ubuntu 18.04 machine, and I am able to produce the Spherical Harmonics Gallery Example both through the regular and envisage Mayavi backends. However, using the default settings of TurboVNC, the rendered scene has artefacts and saving the scene as png yields a black image, which I guess is a consequence of Mesa being used (llvmpipe). If I either start TurboVNC with -extension GLX or if I simply prepend vglrun to the python3 command, which calls VirtualGL, then OpenGL is used and the rendered scene is flawless. I attach the screenshot and saved figures below.
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Install mne-python
You can use mne-python like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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