Pinned Repositories
arl-eegmodels
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
AugmentBrain
In AugmentBrain we investigate the performance of different data augmentation methods for the classification of Motor Imagery (MI) data using a Convolutional Neural Network tailored for EEG named EEGNet.
BCI_MI_Wavelet_CNN
Using wavelet transform to extract time-frequency features of motor imagery EEG signals, and classify it by convolutional neural network
bcidatasetIV2a
This is a repository for BCI Competition 2008 dataset IV 2a fixed and optimized for python and numpy. This dataset is related with motor imagery
CNN-MI-BCI
CNN-SAE program for MI-BCI classification. (Based on "Tabar et al-2016-J Neural Eng. A novel deep learning approach for classification of EEG motor imagery signals")
eeg-adapt
Source Code for "Adaptive Transfer Learning with Deep CNN for EEG Motor Imagery Classification".
EEG-ANALYSIS-FOR-MOTOR-IMAGERY-APPLICATION
EEG-Data-Augmentation-using-Variational-Autoencoder
Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals
EEG-DL
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
EEG-motor-imagery
ECE-GY 9123 Project: GCN-Explain-Net: An Explainable Graph Convolutional Neural Network (GCN) for EEG-based Motor Imagery Classification and Demystification
Shalukshetri's Repositories
Shalukshetri/arl-eegmodels
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
Shalukshetri/AugmentBrain
In AugmentBrain we investigate the performance of different data augmentation methods for the classification of Motor Imagery (MI) data using a Convolutional Neural Network tailored for EEG named EEGNet.
Shalukshetri/BCI_MI_Wavelet_CNN
Using wavelet transform to extract time-frequency features of motor imagery EEG signals, and classify it by convolutional neural network
Shalukshetri/bcidatasetIV2a
This is a repository for BCI Competition 2008 dataset IV 2a fixed and optimized for python and numpy. This dataset is related with motor imagery
Shalukshetri/CNN-MI-BCI
CNN-SAE program for MI-BCI classification. (Based on "Tabar et al-2016-J Neural Eng. A novel deep learning approach for classification of EEG motor imagery signals")
Shalukshetri/eeg-adapt
Source Code for "Adaptive Transfer Learning with Deep CNN for EEG Motor Imagery Classification".
Shalukshetri/EEG-ANALYSIS-FOR-MOTOR-IMAGERY-APPLICATION
Shalukshetri/EEG-Data-Augmentation-using-Variational-Autoencoder
Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals
Shalukshetri/EEG-DL
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
Shalukshetri/EEG-motor-imagery
ECE-GY 9123 Project: GCN-Explain-Net: An Explainable Graph Convolutional Neural Network (GCN) for EEG-based Motor Imagery Classification and Demystification
Shalukshetri/EEG-Motor-Imagery-Classification-CNNs-TensorFlow
EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow
Shalukshetri/EEG-Transformer
i. A practical application of Transformer (ViT) on 2-D physiological signal (EEG) classification tasks. Also could be tried with EMG, EOG, ECG, etc. ii. Including the attention of spatial dimension (channel attention) and *temporal dimension*. iii. Common spatial pattern (CSP), an efficient feature enhancement method, realized with Python.
Shalukshetri/EEGNet
EEGNet remodel performed within internship at Swartz Center for Computational Neuroscience
Shalukshetri/ICASSP2020-2020-code
Shalukshetri/IV-2a
Shalukshetri/motor-imagery
Project to test the accuracy of multiple algorithms published in articles to the EEG binary motor imagery problem
Shalukshetri/motor-imagery-deep-learning
Implementation of Convolutional Recurrent Neural Network (CRNN) to decode motor imagery EEG data.