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