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

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

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

Muse 2016 EEG Headset JavaScript Library (using Web Bluetooth)

Muse 2016 EEG Headset JavaScript Library (using Web Bluetooth)

ERPLAB Toolbox is a free, open-source Matlab package for analyzing ERP data. It is tightly integrated with EEGLAB Toolbox, extending EEGLAB’s capabilities to provide robust, industrial-strength tools for ERP processing, visualization, and analysis. A graphical user interface makes it easy for beginners to learn, and Matlab scripting provides eno…

ERPLAB Toolbox is a free, open-source Matlab package for analyzing ERP data. It is tightly integrated with EEGLAB Toolbox, extending EEGLAB’s capabilities to provide robust, industrial-strength tools for ERP processing, visualization, and analysis. A graphical user interface makes it easy for beginners to learn, and Matlab scripting provides eno…

EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow

EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow

Interactive Brain Playground - Browser based tutorials on EEG with webbluetooth and muse

Interactive Brain Playground - Browser based tutorials on EEG with webbluetooth and muse

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

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

Analyze and manipulate EEG data using PyEEGLab.

Analyze and manipulate EEG data using PyEEGLab.

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

The decoding of continuous EEG rhythms during action observation (AO), motor imagery (MI), and motor execution (ME) for standing and sitting. (IEEE Sensors Journal)