A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

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

Variational mode decomposition (VMD) in Python

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.

A Python library for signal decomposition algorithms

Empirical wavelet transform (EWT) in Python

A python package for extracting EEG features. See article "Unsupervised EEG Artifact Detection and Correction" in Frontiers in Digital Health, 2020.

Analyze and manipulate EEG data using PyEEGLab.

An open source tool for large-scale EEG datasets processing

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

A set of tools to analyze and create charts from Muse EEG devices.

The official implement of Mind's eye: image recognition by EEG via multimodal similarity-keeping contrastive learning.

Machine learning for Anonymous detection of an alcoholic by EEG signals

EEG Brain-Computer Interface to play Flappy Bird and use SSVEP to communicate

A convolutional neural network developed in python using the Keras machine learning framework used to categorize brain signal based on what a user was looking at when the EEG data was collected.

Single trial EEG pipeline at the Abdel Rahman Lab for Neurocognitive Psychology, Humboldt-Universität zu Berlin

It is the task to classify BCI competition datasets (EEG signals) using EEGNet and DeepConvNet with different activation functions. You can get some detailed introduction and experimental results in the link below. https://github.com/secondlevel/EEG-classification/blob/main/Experiment%20Report.pdf

Detect EEG artifacts, outliers, or anomalies using supervised machine learning.