Not supported. Measure 8 EEG channels with Shield PiEEG and RaspberryPi in C library

Wearable (BLE) Brain-Computer Interface, ADS1299 and STM32 with SDK for mobile application

YASA (Yet Another Spindle Algorithm): a Python package to analyze polysomnographic sleep recordings.

CS198-96: Intro to Neurotechnology @ UC Berkeley

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"

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.

EEGraph: Convert EEGs to graphs with frequency and time-frequency domain connectivity measures.

This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.

An R package for processing and plotting of electroencephalography (EEG) data

This project explores the impact of Multi-Scale CNNs on the classification of EEG signals in Brain-Computer Interface (BCI) systems. By comparing the performance of two models, EEGNet and MSTANN, the study demonstrates how richer temporal feature extractions can enhance CNN models in classifying EEG signals

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.

Emotion Recognition, EEG Mapping, Azimuthal Projection Technique, CNN

Python API for Mentalab biosignal aquisition devices

Implementation of Domain Specific Denoising Diffusion Probabilistic Models for Brain Dynamics/EEG Signals

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

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

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