BCI_practice

Learning progress

2020.6.15 Matlab， package EEGLAB

https://sccn.ucsd.edu/wiki/EEGLAB_Wiki

Wiki provides a useful link!

2020.7.6 First Attempt- BCI_Neural_Network_Creation

[https://github.com/Mingyuan-Zhu/BCI_practice/blob/master/7.6%20First%20Atempt-%20BCI_Neural_Network_Creation.ipynb](https://github.com/Mingyuan-Zhu/BCI_practice/blob/master/7.6 First Atempt- BCI_Neural_Network_Creation.ipynb)

This is the steps by steps guide(Original from MNE website, which is a package for BCI preporcessing.We here creating a neural network on the EEG data.

To help us get start it, here we use a dataset provided on the MNE python library. This dataset provided a number of EEG samples that contain 226 data points per sample. Where each sample represents a single trial described in the problem.

[https://github.com/Mingyuan-Zhu/BCI_practice/blob/master/7.9%20Advanced%20topics.md](https://github.com/Mingyuan-Zhu/BCI_practice/blob/master/7.9 Advanced topics.md)

Original guide is from BCI comp

2020.7.10 Preprocessing

[https://github.com/Mingyuan-Zhu/BCI_practice/blob/master/7.10%20%20EEG-preprocessing.ipynb](https://github.com/Mingyuan-Zhu/BCI_practice/blob/master/7.10 EEG-preprocessing.ipynb)

We will be using the Python MNE library in this example:

Computational BCI Class

Matlab tutorial: This is zero to 1 learning guidance from MathWorks

https://github.com/Mingyuan-Zhu/BCI_practice/blob/master/Matlab/mtlab.pdf

7.19 CP Brain

Important Biological Insights of Human Factors Engineering, and also linked with the model like LSTM (long-short term ) in deep learning algorithm.

6.15 to 7.23 Coursera

Computational Neuroscience

by University of Washington

https://github.com/Mingyuan-Zhu/BCI_practice/blob/master/typora-user-images/image-20200723205349001.png

Some Useful Link

Recommended Resources

• Libraries for machine learning for python:

https://pytorch.org/

https://scikit-learn.org/

https://keras.io/

https://www.tensorflow.org/

• Library to analyze EEG data

https://mne.tools/stable/index.html

• Sample EEG data to play with:

http://predict.cs.unm.edu/

• Library to process EEG data with Deep Neural Networks

https://github.com/kylemath/DeepEEG

• Amazing BCI applications

FMRI mind reading

    [https://www.biorxiv.org/content/biorxiv/early/2017/12/30/240317.full.pdf](https://www.biorxiv.org/content/biorxiv/early/2017/12/30/240317.full.pdf)	


    [https://www.nature.com/articles/nn1444](https://www.nature.com/articles/nn1444)

• 1 Hour Master Class on the challenges for Brain Computer Interfaces

https://www.youtube.com/watch?v=5gOhNV6woT0

• For computer scientists: How is analyzing EEG data different from analyzing images?

https://arxiv.org/abs/1901.05498

• Explanation of how to use neural networks on BCI applications

https://towardsdatascience.com/a-beginners-guide-to-brain-computer-interface-and-convolutional-neural-networks-9f35bd4af948?mc_cid=7f0dee93e4&mc_eid=83668a2b82

• Explanation of convolutional neural networks

https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/

• Library to benchmark different algorithms: Mother of All BCI Benchmark

http://moabb.neurotechx.com/docs/

• Simulation of vital signals to use in different analyzes

https://physiology.kitware.com/

• Classes and tutorials on machine learning and cognitive science

https://tomdonoghue.github.io/teaching.html

https://github.com/voytekresearch

• Examples using the commercial EEG Headset Muse

https://github.com/NeuroTechX/eeg-notebooks

• Machine learning library specialized in neuroimaging, particularly fMRI

https://nilearn.github.io/

• Data Structure for fMRI

http://bids.neuroimaging.io/

• Tutorial in Google Colab to analyze fMRI data using the library Keras

https://github.com/NeuroTechX/minc_keras

• Resources for combining classifiers.

Original Paper on Bagging

Original Paper on AdaBoost

Cornell Lecture on Boosting/Bagging

Kaggle Ensemble Guide

SKLearn Ensemble Guide

Various Great Blog Posts