This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow
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Updated
May 2, 2022 - Python
This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow
Mother of All BCI Benchmarks
Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python
EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow
code for AAAI2022 paper "Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification"
Python Brain-Computer Interface Software
A wheelchair controlled by EEG brain signals and enhanced with assisted driving
The programming interface for your body and mind
End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE Transactions on Biomedical Engineering)
Implementation of ConvLSTM in pytorch applied for BCI (Brain Machine Interface) following paper: Convolutional LSTM Network-A Machine Learning Approach for Precipitation Nowcasting
A new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. Paper: https://doi.org/10.1088/1741-2552/ac4430
A Python Library for Implementing Human-Computer Interface Experiments
Brain Computer Interface (BCI) with Neurosky Mindwave Mobile 2 that enables anyone to use computer, mobilephone etc. with his/her thoughts.
Neural networks for EEG-MEG decoding with MNE-python and Tensorflow.
Universal Joint Feature Extraction for P300 EEG Classification Using Multi-Task Autoencoder (IEEE Access)
Documentation for Reproducing & Using the OpenBCI cEEGrid Adapter
Deep Learning pipeline for motor-imagery classification.
Implements High-Gamma dataset decoding using Filter Bank Common Spatial Pattern with rLDA classification and Neural Networks.
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