A list of all public EEG-datasets

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.

This project is for classification of emotions using EEG signals recorded in the DEAP dataset to achieve high accuracy score using machine learning algorithms such as Support vector machine and K - Nearest Neighbor.

a public domain dataset of EEG recordings for music imagery information retrieval

GAN and VAE implementations to generate artificial EEG data to improve motor imagery classification. Data based on BCI Competition IV, datasets 2a. Final project for UCLA's EE C247: Neural Networks and Deep Learning course.

EEG-based emotion classification using DEAP dataset

Emotion recognition can be achieved by obtaining signals from the brain by EEG . This test records the activity of the brain in form of waves. We have used DEAP dataset on which we are classifying the emotion as valance, likeness/dislike, arousal, dominance. We have used LSTM and CNN classifier which gives 88.60 % accuracy to predict the model successfully.

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)

PyTorch EEG emotion analysis using DEAP dataset

Scripts to a) download DEAP EEG dataset b) preprocess its EEG signals and c) perform feature extraction

Emotion Recognition from EEG Signals using the DEAP dataset with 86.4% accuracy. Applied multiple machine learning models and implemented various signal transforming algorithms like the DWT algorithm.

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

Use Vision Transformer to generate Emotion Recognition using the DEAP dataset and EEG Signals.

Public EEG Dataset

GGN model for seizure classification (datasets: TUH EEG seizure TUSZ 1.5.2)

Code for the paper "Multi-Task CNN Model for Emotion Recognition from EEG Brain Maps". DEAP dataset. Python/Keras/Tensorflow 2 Impementation.

The project uses EEG signals from the DEAP Dataset to classify emotions into 4 classes using Ensembled 1-D CNNs, LSTMs and 2D , 3D CNNs and Cascaded CNNs with LSTMs.

JMIR AI'23: EEG dataset processing and EEG Self-supervised Learning

This example shows how to build and train a convolutional neural network (CNN) from scratch to perform a classification task with an EEG dataset.

This repo is for the submission for my Final Year Project on creating Synthetic EEG Data using General Adverserial Neural Networks (GANs) in order to augment existing training datasets to reduce caliberation time in MI-based BCI's.

An open source tool for large-scale EEG datasets processing

This project focuses on data preprocessing and epilepsy seizure prediction using the CHB-MIT EEG dataset. It includes steps like data cleansing, feature extraction, and handling imbalanced datasets, aimed at improving the accuracy of seizure prediction.

OpenNeuro dataset - A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects

Codes and Dataset for the paper titled, "Blink: A Fully Automated Unsupervised Algorithm for Eye Blink Detection in EEG Signals"

EEG Emotion Recognition project, experiment on SEED (SEED-IV), DEAP dataset

Meta-Learning for EEG, Sleep Staging, Transfer Learning, Pre-trained EEG, PSG datasets (IEEE Journal of Biomedical and Health Informatics)

Simply emotion analyse and classify using EEG data based on DEAP dataset, using python and sklearn(SVM,KNN,Tree). 简单的EEG脑电数据情感分析，使用python和DEAP数据集。

EEG-Audio-Video Dataset for Emotion Recognition in Conversations

We constructed an EEG dataset based on imagined speech and performed semantic decoding on it.

hctsa tutorial using the five-class Bonn EEG dataset