E2fNet - A Robust Neural Network for EEG-to-fMRI Synthesis
MATLAB toolbox for SSVEP-based BCI: 19 template-based frequency detection methods including CCA, TRCA, CORRCA, MSI, and multiset CCA, with benchmark code and demos.
EEG signal processing and classification for detecting sleep spindles and classifying motor imagery tasks using Python.
EEG Signal Cleaning Pipeline Management (EEGLAB-based)
Research on human fatigue state analysis induced by gaming based on multimodal data fusion (EEG, EMG, etc.).基于多模态生理信号融合的电子游戏诱发疲劳状态分析
Neuromorphic Code Compiler. Tokenizes EEG brain-neural patterns and compiles neural intent into functional programming syntax (Node.js/Flutter) using deep learning representation architectures.
Bounded negative results for subject-independent EEG affect evaluation under held-out controls.
EEG signal analysis and motor imagery classification for left/right hand movement prediction in BCI applications.
The EEG Classification Model project involves developing a machine learning model to classify brainwave patterns from EEG signals for applications like health monitoring and neurological disorder detection. It focuses on signal processing, feature extraction, and model training using various algorithms.
EEG preprocessing, frequency-band analysis and seizure-segment exploration using MATLAB.
This project aims to classify different sleep states from EEG signal recordings through the use of machine and deep learning models.
From-scratch Python implementations of Common Spatial Pattern (CSP) and Common Spatio-Spectral Pattern (CSSP) for EEG-based motor imagery brain-computer interfaces (MI-BCI), tested on BCI Competition III Dataset IVa.
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