gemeinl
6398803efe
* fixing memory problems memory problems were caused by successively appending predictions to list. fixed by allocating memory beforehand. * created lazy_iterators.py added LoadCropsFromTrialsIterator * created lazy_dataset.py added abstract class as a parent for lazy datasets * Update lazy_iterators.py now saving random state before creating data loader and resetting to random state after every batch. data loader behavior was changed with pytorch upgrade 0.4.0 -> 1.0.0 and broke tests. * Update experiment.py * Update experiment.py * Update lazy_iterators.py * Update lazy_dataset.py * Update iterators.py * Update lazy_iterators.py * Update iterators.py * Update lazy_iterators.py * Update lazy_iterators.py added option to toggle resetting of rng state * Update iterators.py deactivated reset rng state after every batch by default * Update iterators.py accidentally changed traditional iterators. reverting changes * Update lazy_iterators.py deactivated resetting rng after every batch by default * Update experiment.py removed unused variables
Note: The old braindecode repository has been moved to
https://github.com/robintibor/braindevel.
Braindecode
===========
A deep learning toolbox to decode raw time-domain EEG.
For EEG researchers that want to want to work with deep learning and
deep learning researchers that want to work with EEG data.
For now focussed on convolutional networks.
Installation
============
1. Install pytorch from http://pytorch.org/ (you don't need to install torchvision).
2. Install numpy (necessary for resamply installation to work), e.g.:
.. code-block:: bash
pip install numpy
3. Install braindecode via pip:
.. code-block:: bash
pip install braindecode
Documentation
=============
Documentation is online under https://robintibor.github.io/braindecode/
Citing
======
If you use this code in a scientific publication, please cite us as:
.. code-block:: bibtex
@article {HBM:HBM23730,
author = {Schirrmeister, Robin Tibor and Springenberg, Jost Tobias and Fiederer,
Lukas Dominique Josef and Glasstetter, Martin and Eggensperger, Katharina and Tangermann, Michael and
Hutter, Frank and Burgard, Wolfram and Ball, Tonio},
title = {Deep learning with convolutional neural networks for EEG decoding and visualization},
journal = {Human Brain Mapping},
issn = {1097-0193},
url = {http://dx.doi.org/10.1002/hbm.23730},
doi = {10.1002/hbm.23730},
month = {aug},
year = {2017},
keywords = {electroencephalography, EEG analysis, machine learning, end-to-end learning, brain–machine interface,
brain–computer interface, model interpretability, brain mapping},
}