Abstract: Decoding motor imagery (MI) from electroencephalogram (EEG) signals is a cornerstone of brain–computer interface (BCI) systems. However, existing methods often face a critical tradeoff ...

Researchers develop a novel topology-aware multiscale feature fusion network to enhance the accuracy and robustness of EEG-based motor imagery decoding Electroencephalography (EEG) is a fascinating ...

A clean, reproducible starter project for EEG Motor Movement/Imagery using the PhysioNet EEGBCI dataset (a.k.a. EEG Motor Movement/Imagery). It includes scripts to download data with mne, ...

Brain-computer interface (BCI) systems have garnered significant attention in clinical and research settings since the late 20th century. Using brain signals, BCI systems allow users to interact with ...

Abstract: Motor imagery (MI) electroencephalogram (EEG) signals exhibit cross-session variability, which hinders traditional EEG classification models from achieving satisfactory performance in ...

This project implements a Brain-Computer Interface (BCI) system that uses EEG signals to control a Pacman game. The system processes EEG data from motor imagery tasks and uses machine learning to ...

1 Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea 2 Department of Artificial Intelligence, Korea University, Seoul, South Korea In this study, we investigate the ...