eeghdf 0.1

eeg storage in hdf5 + related functions

# 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

• looked at edf and neo formats, see [Neurodata Without Borders](https://github.com/NeurodataWithoutBorders)

• simplier than neo, but may need more of neo’s structures

• compare with [MNE](http://martinos.org/mne/stable/index.html) fif format of mne project to evolve

• look to add fields for clinical report text

• look to add field for montages and electrode geometry

## Simple install for developers - change to the desired python environment ` 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 ` ## To Do

• [x] code to write file, target initial release version is 1000

• [X] initial scripts to convert edf to eeghdf and floating point hdf5

• [x] code to subsample and convert edf -> eeghdf

• [ ] code to write back to edf

• [ ] more visualization code -> push to eegvis

• [x] 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”