https://github.com/danielbinschmid/hdembedding-bci
Science Score: 10.0%
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Low similarity (11.7%) to scientific vocabulary
Last synced: 10 months ago
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Basic Info
- Host: GitHub
- Owner: danielbinschmid
- License: apache-2.0
- Language: Python
- Default Branch: master
- Size: 496 MB
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Fork of MHersche/HDembedding-BCI
Created about 4 years ago
· Last pushed about 4 years ago
https://github.com/danielbinschmid/HDembedding-BCI/blob/master/
Copyright (C) 2020 ETH Zurich, Switzerland. SPDX-License-Identifier: Apache-2.0. See LICENSE file for details. # Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based BrainComputer Interfaces If this code proves useful for your research, please cite our [paper](https://ieeexplore.ieee.org/abstract/document/9073968). > Michael Hersche, Luca Benini, Abbas Rahimi, "Binary Models for Motor-Imagery BrainComputer Interfaces: Sparse Random Projection and Binarized SVM", 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Genova, Italy, 2020, pp. 163-167. More information on the different options can be found [here](https://arxiv.org/abs/1812.05705). ## Getting Started First, download the source code. It is possible to use two different MI datsets, namely the 4-class BCI competition IV2a dataset and a new 3-class data set ,which is made publicly available in this project. The 3-class dataset is stored in 'dataset/3classMI' and can be downloaded together with the source code. When using the 3-class dataset please cite [Saeedi et. al. 2016](https://ieeexplore.ieee.org/abstract/document/7379099). For the 4-class dataset, download the dataset "Four class motor imagery (001-2014)" of the [BCI competition IV-2a](http://bnci-horizon-2020.eu/database/data-sets). Put all files of the dataset (A01T.mat-A09E.mat) into a subfolder within the project called 'dataset/IV2a' or change DATA_PATH in run_hd.py ### Prerequisites - python3.6 - numpy - sklearn - pyriemann - scipy - pytorch4.0 The packages can be installed easily with conda and the _config.yml file: ``` $ conda env create -f _config.yml -n HDenv $ source activate HDenv ``` ### Recreate results For recreation of classification accuracy run the main file ``` python3 run_hd.py ``` ## Author * **Michael Hersche** - *Initial work* - [MHersche](https://github.com/MHersche) ## License Please refer to the LICENSE file for the licensing of our code.
Owner
- Name: danielbin
- Login: danielbinschmid
- Kind: user
- Repositories: 1
- Profile: https://github.com/danielbinschmid
Informatics Master's student at TU Munich.