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

Mother of All BCI Benchmarks

Neural control framework for drones using motor imagery EEG classification. Achieves 73% cross-subject accuracy with PyTorch and enables hands-free drone control through imagined hand/feet movements.

This Cookiecutter template sets up a basic Python project structure, aiming to save you time and to promote best practices. It's ideal for starting new Book projects quickly with a standard setup including a project structure, basic documentation, and necessary configuration files.

Converting real-time EEG into sounds, music and visual effects

A framework for simplying BCI workflow in clinical applications

This interface module is designed for customers who have purchased our equipment. It enables seamless data acquisition from the devices and provides control over operational parameters and device status monitoring.

Python library for real-time gait modulation prediction using multimodal neural and movement data (LFP, EEG, IMU, EMG) — designed for closed-loop DBS systems in Parkinson’s disease.

Python API for Mentalab biosignal aquisition devices

mne-denoise provides narrow-band artefact removal tailored to MNE-Python workflows. It wraps harmonic regression techniques to suppress power-line noise and other oscillatory contaminants while preserving signal rank and interpretability.

Python Brain-Computer Interface Software

Neuroexon presents a hybrid-BCI system that utilizes motor imagery (MI) and steady-state visual-evoked potential (SSVEP) to control a one degree of freedom arm exoskeleton which provides the user with haptic feedback as part of the 2022 NeuroTechX hackathon.

BrainFlow code that sends your brain's relaxation, focus metrics, and machine learned thought commands to vrchat avatar paramaters via OSC.

Shield to Measure EEG with NUCLEO-WB55 STMicroelectronics Development Boards & Kit

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

🧠🔥 Adaptive and inclusive neurotech platform for real-time multimodal data — born from the Olympic flame project.

The Python Noise-Tagging Brain-Computer interface (PyntBCI) library is a Python toolbox for the noise-tagging brain-computer interfacing (BCI) project developed at the Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands

Cortex is an API for communicating with your Emotiv EEG device via web sockets

Routines for an offline c-VEP experiment.