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https://github.com/catalystneuro/ecogvis

Time series visualizer and data processing tools for Electrocorticography (ECoG) signals stored in NWB files, for Python.

https://github.com/catalystneuro/ecogvis

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    2 of 12 committers (16.7%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (14.3%) to scientific vocabulary

Keywords

electrocorticography gui neurodata-without-borders neuroscience nwb open-source pynwb python python3

Keywords from Contributors

electrophysiology json-schemas ndx data-retrieval spike-sorting dash-forms
Last synced: 5 months ago · JSON representation

Repository

Time series visualizer and data processing tools for Electrocorticography (ECoG) signals stored in NWB files, for Python.

Basic Info
  • Host: GitHub
  • Owner: catalystneuro
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 30.8 MB
Statistics
  • Stars: 25
  • Watchers: 3
  • Forks: 18
  • Open Issues: 18
  • Releases: 0
Topics
electrocorticography gui neurodata-without-borders neuroscience nwb open-source pynwb python python3
Created almost 7 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

ecogVIS

Timeseries visualizer and data processing tools for Electrocorticography (ECoG) signals stored in NWB files, for Python.

PyPI version codecov

A collaboration with the Chang Lab.

Installation

From PyPI

bash $ pip install ecogVIS

From source

First download the ecogVIS source code from this repo. bash $ git clone https://github.com/catalystneuro/ecogVIS.git $ cd ecogVIS

If you're installing into an active environment: bash $ pip install -e . -r requirements.txt

You can also create a fresh environment with ecogVIS installed: bash $ conda env create -f make_env.yml

Troubleshooting

  • If you're using macOS 11+ (e.g. Big Sur or Monterey), you may experience errors related to OpenGL. Using Python 3.8 should solve these, and you can use the file make_env_py38.yml to create the correct environment.
  • If you're using macOS 11+ (e.g. Big Sur or Monterey), you may also experience errors with PyQt freezing and not opening the GUI window. Try setting the following environment variable (which can be added to your ~/.bashrc or ~/.bash_profile): bash $ export QT_MAC_WANTS_LAYER=1

Use

After activating the correct environment, ecogVIS can be started from the terminal: bash $ ecogvis

You can also directly pass a file to be opened: bash $ ecogvis --source 'filename.nwb'

ecogVIS can be imported and run from python scripts. If the file does not exist (or if you provide an empty string ''), you'll be prompted to choose a file from a dialog. ```python from ecogvis import main import os

fpath = os.path.join('path_to', 'file.nwb') main(fpath) ```

Features

ecogVIS makes it intuitive and simple to viualize and process ECoG signals. It currently features:

Navigation Seamless visual navigation through long signals from large arrays of electrodes, by mouse-dragging visualization window, control buttons, value fields and keyboard keys.

![](https://github.com/catalystneuro/ecogVIS/blob/master/media/gif_time_navigation.gif?raw=true) ![](media/https://github.com/catalystneuro/ecogVIS/blob/master/media/gif_channel_navigation.gif?raw=true)
Annotations Add, delete, save and load annotations for meaningful comments anywhere in the visualization.

![](https://github.com/catalystneuro/ecogVIS/blob/master/media/gif_annotations.gif?raw=true)
Intervals Add, delete, save, load and create custom intervals types to mark specific points in time, with simple click-drag-release mouse movements.

![](https://github.com/catalystneuro/ecogVIS/blob/master/media/gif_intervals.gif?raw=true)
Bad Channels Mark and un-mark bad channels. Choices are saved in the electrodes group of the current NWB file.

![](https://github.com/catalystneuro/ecogVIS/blob/master/media/gif_badchannels.gif?raw=true)
Signal preprocessing Preprocessing of raw voltage signals, including user-defined Downsampling, CAR and Notch filtering. The resulting processed signals are stored as an LFP object, in the processing group of the current NWB file.

![](https://github.com/catalystneuro/ecogVIS/blob/master/media/gif_preprocessing.gif?raw=true)
Events detection Automatic detection of events in audio recordings for Consonant-Vowel tasks. The audio data should be stored in the NWB file in the following way:
  • Speaker audio - As a TimeSeries object, named 'Speaker CV', in the stimulus group.
  • Microphone audio - As a TimeSeries object, named 'Microphone CV', in the acquisition group.
The resulting detected intervals, 'TimeIntervals_mic' and 'TimeIntervals_speaker', are saved as TimeIntervals objects in the intervals group of the current NWB file and can be used later for ERP analysis. A preview allows for testing of the detection parameters before running it for the whole duration of the audio signals.

![](https://github.com/catalystneuro/ecogVIS/blob/master/media/gif_event_detection.gif?raw=true)
High Gamma Estimation of high gamma analytic amplitude, with the average of user-defined specific bands. The results are saved as a TimeSeries object, named 'high_gamma', in the processing group of the current or of a new NWB file.

![](https://github.com/catalystneuro/ecogVIS/blob/master/media/gif_highgamma.gif?raw=true)
Event-Related Potentials Grid visualization of high gamma ERP calculated in reference to:
  • Stimulus (speaker) or response (microphone) time intervals
  • Onset or offset points
The grid items are coloured to mark specific cortical areas and can be rotated to correspond anatomically to them. Emphasis can be given to specific areas of interest and double-clicking an item allows for fast inspection of the single electrode's ERP in detail.

![](https://github.com/catalystneuro/ecogVIS/blob/master/media/gif_erp.gif?raw=true)
Periodograms Grid visualization of Periodograms:
  • Raw and preprocessed data
  • FFT and Welch methods
  • Individual devices
The grid items are coloured to mark specific cortical areas and can be rotated to correspond anatomically to them. Emphasis can be given to specific areas of interest and double-clicking an item allows for fast inspection of the single electrode's Periodogram in detail.

![](https://github.com/catalystneuro/ecogVIS/blob/master/media/gif_periodograms.gif?raw=true)
Spectral Decomposition Analytic signal amplitude estimation by Hilbert transform of user-defined frequency bands.

WARNING: This function will manipulate an array of size (nSamples, nChannels, nBands), which might be in the order of gigabytes and demand a large memory to operate and is likely to produce a large file.

![](https://github.com/catalystneuro/ecogVIS/blob/master/media/gif_decomposition.gif?raw=true)

Plus

  • Select electrodes from specific brain areas
  • Easy moving between block files for the same subject

Owner

  • Name: CatalystNeuro
  • Login: catalystneuro
  • Kind: organization
  • Email: hello@catalystneuro.com

GitHub Events

Total
  • Watch event: 3
  • Issue comment event: 1
  • Fork event: 1
Last Year
  • Watch event: 3
  • Issue comment event: 1
  • Fork event: 1

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 395
  • Total Committers: 12
  • Avg Commits per committer: 32.917
  • Development Distribution Score (DDS): 0.552
Top Committers
Name Email Commits
luiztauffer l****r@h****m 177
root l****r@h****m 97
Ben Dichter b****r@g****m 30
Armin Najarpour Foroushani a****r@g****m 28
Emily P Stephen e****4@g****m 21
Jessie j****u@b****u 18
jgmakin j****n@g****m 15
Jessie j****5@g****m 5
jersaal j****l@g****m 1
Laura Gwilliams l****s@u****u 1
Jessie Liu j****u@J****l 1
Cody Baker 5****D@u****m 1
Committer Domains (Top 20 + Academic)
ucsf.edu: 1 berkeley.edu: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time
  • Total issues: 46
  • Total pull requests: 56
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 12 days
  • Total issue authors: 10
  • Total pull request authors: 10
  • Average comments per issue: 1.98
  • Average comments per pull request: 1.09
  • Merged pull requests: 36
  • Bot issues: 0
  • Bot pull requests: 4
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • bendichter (14)
  • jessierliu (10)
  • emilyps14 (9)
  • kristinksellers (3)
  • luiztauffer (3)
  • LauraGwilliams (2)
  • jthermiz (2)
  • mattkleonard (1)
  • cbin-cnl (1)
  • JanevaD (1)
Pull Request Authors
  • Armin12 (23)
  • luiztauffer (14)
  • emilyps14 (6)
  • jessierliu (3)
  • dependabot[bot] (3)
  • bendichter (2)
  • hans (1)
  • jgmakin (1)
  • jersaal (1)
  • LauraGwilliams (1)
Top Labels
Issue Labels
Pull Request Labels
dependencies (3)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 51 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 6
  • Total maintainers: 2
pypi.org: ecogvis

Timeseries visualizer for Electrocorticography (ECoG) signals stored in NWB files.

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 51 Last month
Rankings
Forks count: 9.1%
Dependent packages count: 10.0%
Stargazers count: 12.6%
Average: 15.7%
Dependent repos count: 21.7%
Downloads: 24.8%
Maintainers (2)
bendichter luiz_tauffer
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • PyQt5 ==5.15.0
  • PyQtWebEngine ==5.15.0
  • PyYAML ==5.3.1
  • cycler ==0.10.0
  • h5py ==2.10.0
  • matplotlib ==3.2.1
  • ndx-bipolar-scheme >=0.3.1
  • ndx-ecog ==0.1.1
  • ndx-hierarchical-behavioral-data ==0.1.1
  • ndx-icephys-meta ==0.1.0
  • ndx-spectrum ==0.2.2
  • ndx-survey-data ==0.1.0
  • numpy ==1.18.2
  • pandas ==1.0.3
  • process_nwb ==0.1.0
  • pynwb ==1.3.2
  • pyopengl ==3.1.5
  • pyqtgraph ==0.10.0
  • scipy ==1.4.1
setup.py pypi
  • PyQt5 *
  • PyQtWebEngine *
  • PyYAML *
  • cycler *
  • h5py *
  • matplotlib *
  • ndx-bipolar-scheme *
  • ndx-ecog *
  • ndx-hierarchical-behavioral-data *
  • ndx-icephys-meta *
  • ndx-spectrum *
  • ndx-survey-data *
  • numpy *
  • pandas *
  • process_nwb *
  • pynwb ==1.3.2
  • pyopengl *
  • pyqtgraph *
  • scipy *
  • tqdm *