Popular GitHub Repositories for Eeg Signals

code for AAAI2022 paper "Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification"

Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.

Code to accompany our International Joint Conference on Neural Networks (IJCNN) paper entitled - Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification

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

Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals

Code to accompany our International Conference on Pattern Recognition (ICPR) paper entitled - Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI.

ADHDeepNet is a model that integrates temporal and spatial characterization, attention modules, and explainability techniques, optimized for EEG data ADAD diagnosis. Neural Architecture Search (NAS), Hyper-parameter optimization, and data augmentation are also incorporated to enhance the model's performance and accuracy.

EEG-DIF: Early Warning of Epileptic Seizures through Generative Diffusion Model-based Multi-channel EEG Signals Forecasting

This curated list highlights the latest breakthroughs in EEG and AI integration, providing a user-friendly guide for researchers, students, and hobbyists to explore advancements and applications in this exciting field.

A Novel Adversarial Approach for EEG Dataset Refinement: Enhancing Generalization through Proximity-to-Boundary Scoring

Code to run experiments generating synthetic time series medical data

Linear Predictive Coding based Tokenizer for self-supervised learning of time series via BERT. Article: https://arxiv.org/abs/2408.07292

NeuroAd uses EEG data to predict how audiences will engage with advertisements. By analyzing brainwave patterns, it provides insights into ad performance, helping optimize content before launch. This project combines neuroscience with machine learning for a data-driven approach to enhancing ad effectiveness.

Synthetic Data Generation Algorithms (VAE-GAN-Diffusion Model-LSTM-Copula)

[NeurIPS2024] Neural Information Processing Systems(2024) - Workshop on GenAI for Health: Potential, Trust and Policy Compliance (GenAI4Health)

[NeurIPS2024] Neural Information Processing Systems(2024) - Workshop on Causality and Large Models (CaLM)