Workshop on standardized Brain-Computer Interface Framework
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Updated
May 22, 2024 - C#
Workshop on standardized Brain-Computer Interface Framework
Deep Learning toolbox for EEG based Brain-Computer Interface signals decoding and benchmarking
An open software package to develop BCI based brain and cognitive computing technology for recognizing user's intention using deep learning
Code for fitting EEG data with Wishart and t-Wishart distributions
This repository contains code for analyzing Steady-State Visually Evoked Potential (SSVEP) signals recorded from EEG data. The goal of this analysis is to determine the stimulation frequency from the EEG signals recorded in each test. Two methods are employed: Frequency Content Plotting and Canonical Correlation Analysis (CCA).
Unity gaming project for the BR41N.io Hackathon at g.tec's Spring School 2023. Unicorn Unity Interface Hybrid Black and real-time EEG data.
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."
This is a Simple Blinking Window with Adjustable Frequency for SSVEP Stimulation.
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.
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.
Code to accompany our 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) paper entitled - On the classification of SSVEP-based dry-EEG signals via convolutional neural networks.
Master Thesis: Evaluating a Binocular SSVEP Paradigm in Virtual Reality
Simulations of stimulus-stimulus transfer based on time-frequency-joint representation in SSVEP-based BCIs. The proposed stimulus-stimulus transfer method has been published in IEEE TBME (DOI: 10.1109/TBME.2022.3198639)
Code repository for the work done by Team 16 for their Final Year Design Project (FYDP) at the University of Waterloo for Biomedical Engineering
Code to accompany our International Conference on Pattern Recognition (ICPR) paper entitled - Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI.
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