eeghdf 0.2.3

eeghdf is a module for reading a writing EEG data into the hdf5 format

eeghdf

Project to develop a easily accessible format for storing EEG in a way that is easy to access for machine learning.

Features

Features derived from hdf5 format:

• hdf5 offers reliable, checksummed and compressed storage of digital EEG which was designed for long-term storage of data
• hdf5 is supported widely C, C++, javascript, python, julia, matlab,
• eeghdf offers a numpy-like interface to data without requiring the whole file to be loaded in memory
• efficient reading (the whole file is not read into memory to access data)
• cloud enabled direct streaming from S3 buckets via the rcos3 driver
• "self documenting" and extensible
• advanced features: parallel readers/single writer, MPI, streaming supported

Additional goals/features:

• build set of tools to visualize and analyze EEG based upon this format, visualization
• easy convertion to other formats: first target is mne-python "raw" format, BIDS-EEG next?

Alternatives, background research and future goals

• looked at edf and neo formats, see Neurodata Without Borders. Compare with XDF.
  • simplier than neo, but may need more of neo's structures as use grows
• ONE format
• compare with MNE fif format of mne project to evolve

future goals

• look to support multiple records and different sampling rates
• look to add fields for clinical report text
• look to add field for montages and electrode geometry
• "extension" group

installation

pip install eeghdf

Simple install for developers

This assumes you want to make changes to the eeghdf code.

• change to the desired python virtual environment
• make sure you have git and git-lfs installed

git clone https://github.com/eegml/eeghdf.git 
cd eeghdf

python setup-dev.py develop

Re-sampling

There are many ways to resample signals. In my examples I used an approach based upon libsamplerate because it seemed to give accurate results. Depending on your platform there are many options. Recently I have been suing pytorch based tools a lot, torchaudio has resamplinge tools and librosa is looks very impressive.

Installation will vary but on ubuntu 18.04 I did:

sudo apt install libsamplerate-dev
pip install git+https://github.com/cournape/samplerate/#egg=samplerate

Ultimately I will move the resampling code out of this repo. Maybe put it in eegml-signal

To Do

• code to write file, target initial release version is 1000
• initial scripts to convert edf to eeghdf and floating point hdf5
• code to subsample and convert edf -> eeghdf
• code to write back to edf
• more visualization code -> push to eegvis
• add convenience interface to phys_signal with automagic conversion from digital->phys units - should this use a subclass of numpy?
• add study admin code to record info (do not seem to include this now, e.g. EEG No like V17-105)
• code to clip and create subfiles
  • allow patient info to propagate
  • hash list/tree of history of file so that can track provenance of waveforms if desired
  • clip and maintain correct (relative) times
• consider how to handle derived records: for example the downsampled float32 records "frecord200Hz"