An open software package to develop BCI based brain and cognitive computing technology for recognizing user's intention using deep learning

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

Classification toolbox for ERP and SSVEP based BCI data

The SSVEP Keyboard works with the icibici hardware. Once connected you should be able to look at keys and it will type them for you.

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

SSVEP-based BCI recording of 12 subjects operating an upper limb exoskeleton during a shared control task. The exoskeleton is either controlled with a touchless interface detecting hand poses or with BCI.

Python package for SSVEP datasets and algorithms. Corresponding paper has been accepted by IEEE TNSRE (DOI: 10.1109/TNSRE.2024.3424410)

Using multi-task learning to capture signals simultaneously from the fovea efficiently and the neighboring targets in the peripheral vision generate a visual response map. A calibration-free user-independent solution, desirable for clinical diagnostics. A stepping stone for an objective assessment of glaucoma patients’ visual field.

A basic demonstration how to use Python, MNE, and PyTorch to analyze EEG signal.

Leverage SSVEP to control drone in real time

EEG BCI Real-Time Applications: Contains real-time demonstrations of BCI applications

The source code of the OACCA introduced in our IEEE TBME paper (10.1109/TBME.2021.3133594)

Matlab code of our IEEE TASE paper "Wong, C. M., Wang, Z., Rosa, A. C., Chen, C. P., Jung, T. P., Hu, Y., & Wan, F. (2021). Transferring subject-specific knowledge across stimulus frequencies in SSVEP-based BCIs. IEEE Transactions on Automation Science and Engineering, 18(2), 552-563."

SSVEP Brain Computer Interface - Example code for real-time detection of SSVEP using the Canonical Correlation Analysis (CCA) code in real-time. Implemented using OpenViBE and Python

uniBrain Speller: A one-stop, user-friendly, open-source brain-computer interface speller software developed by Prof. Gao Xiaorong's team at Tsinghua University, China, designed for various users including patients, researchers, and practitioners.

High School SSVEP-BCI Research Project to improve classification accuracy of captured EEG signals

C# program to superpose SSVEP and c-VEP flickers on top of other graphical interfaces

Code to accompany our International Conference on Robotics and Automation (ICRA) paper entitled - Using variable natural environment brain-computer interface stimuli for real-time humanoid robot navigation.

Data mining based approach to study the effect of caffeinated coffee on SSVEP brain signals. https://doi.org/10.1016/j.compbiomed.2019.103526