https://github.com/philkes/ml-bci
Machine Learning based Brain Computer Interface (BCI) by analyzing EEG Data using PyTorch
https://github.com/philkes/ml-bci
eeg-classification eeg-data eegnet machine-learning python pytorch
Last synced: 7 months ago
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Machine Learning based Brain Computer Interface (BCI) by analyzing EEG Data using PyTorch
- Host: GitHub
- URL: https://github.com/philkes/ml-bci
- Owner: PhilKes
- Created: 2021-02-02T18:37:22.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-01-12T17:13:27.000Z (almost 3 years ago)
- Last Synced: 2025-02-01T04:31:47.355Z (9 months ago)
- Topics: eeg-classification, eeg-data, eegnet, machine-learning, python, pytorch
- Language: Python
- Homepage:
- Size: 57.8 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ML-BCI
This repo contains the implementation for my bachelor thesis "Deep Learning based Motor Imagery Brain Computer Interface" for the [THU Ulm](https://studium.hs-ulm.de/en)
You can further read about the project's topic in the published paper
[Investigation of a Deep-Learning Based Brain–Computer Interface With Respect to a Continuous Control Application](https://ieeexplore.ieee.org/document/9978624)
## Machine Learning based Brain-Computer Interface Framework
* EEGNet Implementation in PyTorch
* Training, Testing, Benchmarking on [PhysioNet Dataset](https://physionet.org/content/eegmmidb/1.0.0/)
, [BCIC IV 2a](http://www.bbci.de/competition/iv/#dataset2a)
, [LSMR21](https://figshare.com/articles/dataset/Human_EEG_Dataset_for_Brain-Computer_Interface_and_Meditation/13123148)
* Proposed Implementation for use on NVIDIA Jetson Nano
___
! Datasets ARE NOT INCLUDED in this repository !
- PHYS: Dataset is automatically loaded via the MNE Library
- BCIC: Has to be downloaded manually (see bcic_data_loading.py main method)
- LSMR21: Has to be downloaded via the scripts/lsmr21_download_convert.py script
Change the Location of the Datasets in `paths.py`'s `datasets_folder` variable
### main.py
Main Script to run Training/Benchmarking of EEGNet
`main.py -train`
* n-Class-Classification Training
* K-Fold Crossvalidation
* Saving results and trained model in _./results/{DateTime/Name}/training_
* Dataset Selection with `--dataset` argument
`main.py -benchmark`
* Inference Benchmarking in batches with specified trained model (default size: 16)
* TensorRT optimization possible with `--trt` flag
* Saving results (Batch Latency, Inference time per trial) in _./results/{model_path}/benchmark_
`main.py -train_ss`
* Transfer Learning for further subject-specific Training of pretrained model
* Select pretrained model with `--model` argument
* Subject to train on with `--subject` argument
* Saving subject-specific trained model in _./results/{model_path}/training_ss/S{subject}_
`main.py -live_sim`
* Simulation of a live EEG run classification of a single subject with subject-specific trained model
* Select subject-specific trained model with `--model` argument
* Plotting the run in _./results/{model_path}/training_ss/S{subject}/live_sim_
`main.py --help` for all arguments
___
### config.py
Global Default Configuration/Settings
* Training/Benchmarking Settings
* Bandpassfilters
* EEG Channel Configurations for sensorimotor Events
___
### /machine_learning/models/eegnet.py
PyTorch Implementation of EEGNet
Original Source:
[xiaywang/q-eegnet_torch](https://github.com/xiaywang/q-eegnet_torch/blob/0f467e7f0d9e56d606d8f957773067bc89c2b42c/eegnet.py)
### /machine_learning/modes.py
Main loops for
* EEGNet Training + Testing on Physionet Dataset using 5-Fold CV
* Subject-specific Training with Transfer Learning
* Benchmarking of Inferencing over Physionet Dataset with pretrained model
* Live Simulation of real time Classification on Physionet Dataset Run
### /machine_learning/inference_training
Main methods for
* Training
* Testing
* Benchmark
* Predicting on Batches of Data
___
### /data/datasets/{...}_data_loading.py's
* Data Loaders for the used Datasets
* Wrap Datasets in PyTorch Dataloader
### /data/datasets/physionet_dataset.py
* All important Properties of the Physionet Motorimagery Dataset
* List of available Subjects, Tasks, Runs
* Default Values for Epoching (tmin, tmax, ...)
### /data/datasets/bcic_dataset.py
* All important Properties of the BCIC Motorimagery Dataset
* List of available Subjects, Tasks, Runs
* Default Values for Epoching (tmin, tmax, ...)
### /data/datasets/lsmr21_dataset.py
* All important Properties of the LSMR21 Motorimagery Dataset
* List of available Subjects, Tasks, Runs
* Default Values for Epoching (tmin, tmax, ...)
### /data/data_utils.py
* TrialsDataset class for usage with PyTorch Dataloader
* Utility methods ofr preprocessing data
___
## Scripts
Have to be executed as python modules with:
```python3 -m scripts. ```
### example_all_modes.py
* Showcases usage of all available modes in a single script
* Gives overview on how to use main.py with its arguments in a Terminal
### batch_training.py
* Can be used for Batchruns of Training Runs
* Training Configurations editable in Python Dictionary
* Creates .csv and .txt files containing Results of Training Batchruns
### neural_responses_training.py
* Can be used to analyze influence of different Frequency bands
* Test neural responses in different time slices
### best_fold_frequency_test.py
* Can be used to Test trained model of neural_responses_training.py
* Tests different Fx-filtered Test Data of trained Model
#### bench_all_confs.py
* Runs _main.py_ with all possible Configurations in benchmark mode (/w TRT (fp16/32))
* Saves results in parent folder _./results/{model_path}/benchmark/_
* Benchmarking with different Batch Sizes with `--bs` argument
#### visualize_bench_all.py
* Plots and saves Results from _bench_all_confs.py_ Runs as .png
* `--model` specifies the folder location of the _results.npz_ file
#### /paths.py
* All relevant paths, results folder locations, file names, ...
#### /util/plot.py
* Plot Results with Matplotlib
#### /util/configs_results.py
* Handles storing results/config .txt files
#### python_test.py
Python Playground for testing, has no further use
---
## Libraries
Use `python3 -m pip install -r requirements.txt` to ensure all necessary libraries are installed
For usage of `-benchmark` also ensure TensorRT is correctly setup on the system
[Numpy](https://numpy.org/)
[PyTorch](https://pytorch.org/)
[PyCuda](https://documen.tician.de/pycuda/)
[MNE](https://mne.tools/stable/index.html)
[torch2trt](https://github.com/NVIDIA-AI-IOT/torch2trt)
[Matplotlib](https://matplotlib.org/)
[Pandas](https://pandas.pydata.org/)