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https://github.com/okbalefthanded/pylpov
EEG signal Offline/Online processing toolbox
https://github.com/okbalefthanded/pylpov
brain-computer-interface eeg machine-learning openvibe python signal-processing
Last synced: 5 days ago
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EEG signal Offline/Online processing toolbox
- Host: GitHub
- URL: https://github.com/okbalefthanded/pylpov
- Owner: okbalefthanded
- License: apache-2.0
- Created: 2019-03-31T18:59:25.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-03-30T21:35:34.000Z (10 months ago)
- Last Synced: 2024-05-14T00:18:10.644Z (8 months ago)
- Topics: brain-computer-interface, eeg, machine-learning, openvibe, python, signal-processing
- Language: Python
- Homepage:
- Size: 130 KB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# pyLpov : python-LARESI-processing-OpenVibe
- Accompanying [LARESI_BCI](https://github.com/okbalefthanded/Laresi_BCI) Python scripts for offline/online analysis and classification of EEG data in OpenVibe
---
### Version requirements
- [OpenVibe <= 3.0.0](http://openvibe.inria.fr/downloads/)
- Python 3.7 and above
---
## Installation
### Option 1 : build from source
First, clone repo from github:
```
git clone https://github.com/okbalefthanded/pyLpov.git
```
Then,
```
cd pyLpov
pip install -r requirements.txt
python setup.py install
```
---
### Option 2 : using pip
```
pip install git+https://github.com/okbalefthanded/pyLpov.git
```
---
### CPU Inference Acceleration
In online processing with Deep Neural Networks, Keras/TensorFlow models are supported. When only CPU (Intel CPUs) is used for inference it is recommanded to install and use Intel's OpenVINO toolkit.
[OpenVINO installation guide](https://docs.openvinotoolkit.org/latest/openvino_docs_install_guides_installing_openvino_windows.html)
---
## Usage
### Offline processing
#### - Feature engineering pipeline
The API is built in a way that provides automatic processing through the use of [YAML](https://wiki.python.org/moin/YAML) configuration files and [Scikit-learn](https://scikit-learn.org/stable/) classes : [Pipeline](https://scikit-learn.org/stable/modules/compose.html#pipeline) and [Estimators](https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html?highlight=estimator#sklearn.base.BaseEstimator) (inspired from the API of [MOABB](https://github.com/NeuroTechX/moabb))
A configuration file specifies the pipline chain of operations from preprocessing to feature extractors to classifiers.
#### Using CLI
see ```cmd_tuto.bat``` on how to set and execute the offline analysis, and follow ```sa_hybrid_train.py``` in pyLpov/scripts/standalone on how to set an automatice processing script.
#### - End-To-End Deep Neural Networks
Trained Keras models saved in H5 and TF2 SavedModl format are supported.
### Online processing
- Make sure the python scripting box is available in the OpenVibe Designer Scripting tab.
- Add a python scripting box to the scenario.
- Follow this tutorial for correct usage of python scripts [Python in Openvibe](http://openvibe.inria.fr/tutorial-using-python-with-openvibe/)
- Add one of the online scripts from pyLpov/scripts/scenarios to your experiment scenario, for example use ```ssvep_py_online.py``` for SSVEP online detection.
---
## Methods available
As pyLpov API relies heavily on scikit-learn, any built-in classifier or regressor can be easily defined in the pipeline, the same goes for any 3rd-party methods developed with scikit-learn's estimators. Nevertheles we keep adding specific BCI methods, the following list shows the available methods so far:
### Event-Related Potentials (ERP)
- Downsample and vector concatenation.
- [EPFL](http://infoscience.epfl.ch/record/101093) approach
### Steady-State Visual Evoked Potentials (SSVEP)
- CCA and [ITCCA](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140703)
- [MLR](https://ieeexplore.ieee.org/abstract/document/7389413/)
---
## Citation
```
@misc{bekhelifi2022fast,
title={Towards Fast Single-Trial Online ERP based Brain-Computer Interface using dry EEG electrodes and neural networks: a pilot study},
author={Okba Bekhelifi and Nasr-Eddine Berrached},
year={2022},
eprint={2211.10352},
archivePrefix={arXiv},
primaryClass={eess.SP}
}
```
---
## Acknowledgment