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

Project description

ecogVIS

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

A collaboration with the Chang Lab.

Installation

$ pip install ecogVIS

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

$ ecogvis

You can also directly pass a file to be opened:

$ 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.

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.

Annotations

Add, delete, save and load annotations for meaningful comments anywhere in the visualization.

Intervals

Add, delete, save, load and create custom intervals types to mark specific points in time, with simple click-drag-release mouse movements.

Bad Channels

Mark and un-mark bad channels. Choices are saved in the electrodes group of the current NWB file.

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.

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.

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.

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.

Periodograms

Grid visualization of Periodograms:

Raw and preprocessed data
FFT and Welch methods
Individual devices

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.

Plus

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

Project details

Release history Release notifications | RSS feed

1.2.2

Jul 16, 2020

1.2.1

Jul 14, 2020

1.2.0

Jul 10, 2020

This version

1.1.0

Jun 3, 2020

1.0.1

Mar 31, 2020

1.0.0

Mar 31, 2020

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ecogvis-1.1.0.tar.gz (81.6 kB view details)

Uploaded Jun 3, 2020 Source

Built Distribution

ecogvis-1.1.0-py3-none-any.whl (93.7 kB view details)

Uploaded Jun 3, 2020 Python 3

File details

Details for the file ecogvis-1.1.0.tar.gz.

File metadata

Download URL: ecogvis-1.1.0.tar.gz
Upload date: Jun 3, 2020
Size: 81.6 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.7

File hashes

Hashes for ecogvis-1.1.0.tar.gz
Algorithm	Hash digest
SHA256	`d5d184bffc74b93197aa9f26bfe2bdcfaf0d18f935e3f1f433a5e15cbad289f5`
MD5	`a459549f7e1539ffcbe9f464e5e49dcd`
BLAKE2b-256	`70452d5017737fbb325a161579ed58a8bc38091b5b51fa8762101785626820a0`

See more details on using hashes here.

File details

Details for the file ecogvis-1.1.0-py3-none-any.whl.

File metadata

Download URL: ecogvis-1.1.0-py3-none-any.whl
Upload date: Jun 3, 2020
Size: 93.7 kB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.1.3.post20200330 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.7.7

File hashes

Hashes for ecogvis-1.1.0-py3-none-any.whl
Algorithm	Hash digest
SHA256	`aed31132c19bfdda3852dc4535d56773a1139a50ddac87cc5bacfc58169b7883`
MD5	`806ba7dbea651ae416f8532989507fcd`
BLAKE2b-256	`033d0ae1efb23e235b7ba328b501f53345191db79028eb2db4926fa2060945a2`