Skip to main content

Read and write EEG data in the BrainVision Core data format

Project description

Read and write EEG data in the BrainVision Core data format

This is a Python implementation to read and write EEG data in the BrainVision Core data format as defined by BrainProducts and as used in BIDS.

The BrainVision Core data format consists of three files: the .vhdr file with header information, the .vmrk with markers or events, and the data in a file that usually has the extension .eeg. All files have to be in the same folder.

The information from the header and marker is not parsed but retained as a dictionary with strings. Below some examples are given to get for example the channel names as list

This implementation can read from 16 and 32 bit integer formats and 32 bit float formats. It supports multiplexed and vectorized orientations. The data is returned as a channels-by-samples Numpy array with float32 values.

Example

The following example reads data from disk.

import brainvision

# read the data
(vhdr, vmrk, eeg) = brainvision.read('test/input.vhdr')

# check the size of the data
(nchans, nsamples) = eeg.shape
print(nchans, nsamples)

# parse the header
nchans = int(vhdr['Common Infos']['NumberOfChannels'])
fsample = 1000000.0 / float(vhdr['Common Infos']['SamplingInterval'])
labels = [item.split(',')[0] for item in vhdr['Channel Infos'].values()]
units  = [item.split(',')[3] for item in vhdr['Channel Infos'].values()]
print(nchans, fsample, labels, units)

# parse the markers
type        = [item.split(',')[0] for item in vmrk['Marker Infos'].values()]
description = [item.split(',')[1] for item in vmrk['Marker Infos'].values()]
sample      = [int(item.split(',')[2])-1 for item in vmrk['Marker Infos'].values()]   # in data points, 0-based
duration    = [int(item.split(',')[3])   for item in vmrk['Marker Infos'].values()]   # in data points
channel     = [int(item.split(',')[4])   for item in vmrk['Marker Infos'].values()]   # note that this is 1-based
print(type, description, sample, duration, channel)

The following example constructs data from scratch and writes it to disk. Upon writing the header and markerfile, the vhdr and vmrk dictionaries will be validated to ensure that they contain the required fields.

import numpy as np
import brainvision

vhdr = {'Common Infos': {'Codepage': 'UTF-8', 'DataFile': 'output.eeg', 'MarkerFile': 'output.vmrk', 'DataFormat': 'BINARY', 'DataOrientation': 'MULTIPLEXED', 'NumberOfChannels': '1', 'SamplingInterval': '1000'}, 'Binary Infos': {'BinaryFormat': 'FLOAT_32'}, 'Channel Infos': {'Ch1': '1,,0.5,µV'}}

vmrk = {'Common Infos': {'Codepage': 'UTF-8', 'DataFile': 'output.eeg'}, 'Marker Infos': {'Mk1': 'New Segment,,1,1,0'}}

nchans = 1
nsamples = 1000
rng = np.random.default_rng()
eeg = rng.standard_normal((nchans, nsamples))

# write the data
brainvision.write('output.vhdr', vhdr, vmrk, eeg) 

Known limitations

This implementation currently cannot read ASCII data.

This implementation currently cannot deal with little to big endian conversions, hence reading and writing the binary data is only supported on little endian architectures. Apple silicon and Intel-based Mac computers are both little endian. Also Raspberry PI's ARM and Intel NUC's processors are little endian.

Copyright

Copyright (C) 2024, Robert Oostenveld

This code is dual-licensed under the BSD 3-Clause "New" or "Revised" License and under the GPLv3 license, the choice is up to you.

When you make changes/iomprovements, please share them back to us so that others benefit as well.

Acknowledgements

The test data that is included to demonstrate the functionality and to test the reading and writing originates from the pybv package.

See also

Project details


Download files

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

Source Distribution

brainvision-0.2.tar.gz (17.9 kB view details)

Uploaded Source

Built Distribution

brainvision-0.2-py3-none-any.whl (18.7 kB view details)

Uploaded Python 3

File details

Details for the file brainvision-0.2.tar.gz.

File metadata

  • Download URL: brainvision-0.2.tar.gz
  • Upload date:
  • Size: 17.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for brainvision-0.2.tar.gz
Algorithm Hash digest
SHA256 6c2cc4374555a253b2ae95c31cace127ba8f121a4fcb9772fafbad5cb6ca3b55
MD5 20d03c8dcde552c26cefeec3be1ba743
BLAKE2b-256 014a32756c603c2dcf96369e552951cbaed46992cf0d4489dd0ed6b4e95b73bc

See more details on using hashes here.

File details

Details for the file brainvision-0.2-py3-none-any.whl.

File metadata

  • Download URL: brainvision-0.2-py3-none-any.whl
  • Upload date:
  • Size: 18.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for brainvision-0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 734249272420a6316e5b9d1a292c74c0f34898bd6599c8a192ea261774b53a18
MD5 134d4114525f5ba9e88a7191e0c36c47
BLAKE2b-256 4ee8df7fb1b131a7f16b2c77412e695f448b5b1a0d51983bbb1514f5f3f81621

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page