eeghdf 0.2.2

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.

• hdf5 based format with the following advantages:

  • industry standard format, supported in many languages (C, python, javascript, matlab..)
  • compression, checksums
  • efficient reading (the whole file is not read into memory to access data)
  • "self documenting" and extensible
  • advanced features: parallel readers/single writer, MPI, streaming supported

• 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

• compare with MNE fif format of mne project to evolve

• looke 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

Simple install for developers

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

git clone https://github.com/eegml/eeghdf.git 
pip install -e eeghdf

• or if you just want to install as a requirement into a virtual env. Put this into your requirements.txt. The repo will be cloned into ./src/eeghdf and installed

-e git+https://github.com/eegml/eeghdf#egg=eeghdf

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

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
• 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"