PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

🙌Kart of 232+ projects based on machine learning, deep learning, computer vision, natural language processing and all. Show your support by ✨ this repository.

Python脑电数据处理中文手册 - A Chinese handbook for EEG data analysis based on Python

EEGdenoiseNet, a benchmark dataset, that is suited for training and testing deep learning-based EEG denoising models, as well as for comparing the performance across different models.

Processed the DEAP dataset on basis of 1) PSD (power spectral density) and 2)DWT(discrete wavelet transform) features . Classifies the EEG ratings based on Arousl and Valence(high /Low)

Deep neural network for eye movement detection

Using Deep Learning for Emotion Classification on EEG signals (SEED Dataset). CNN, RNN, Hybrid model, and Ensemble

Project to compare different eyetrackers

This repository contains the code for emotion recognition using wavelet transform and svm classifiers' rbf kernel.

In this Basic Tutorial, I have used 1DCNN for EEG classification using random dataset, You can use your own dataset

Labs for CAI

An EEG-based emotion recognition system using Simple Recurrent Units(SRU) in Pytorch library. It identifies three emotions: positive, neutral and negative. It uses SEED dataset which consist of EEG…

Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. To achieve possible solution for problem we first analyze the SSVEP BCI based …

Scratch Detection Assignment for a student position in the data science team at NI

EEG classification using deep learning

Electrooculogram signal processing for neuropsychology applications

Task designing + EEG processing

To identify the emotions through EEG

Sleep stage classification using EEG and EOG signals

Predict brain seizures with Machine Learning

Framework for drowsiness detection in driving scenarios using Brain-Computer Interfaces

: use machine learning techniques for the accurate prediction of eye movement patterns (up-down-left-right-blink) using data acquired from EOG signals. In addition to develop a maze and the model s…