The goal of this project is to classify EEG signals recorded during motor imagery tasks, i.e. when a subject imagines moving a limb (left or right hand). By decoding these imagined movements from ...
Motor imagery (MI) electroencephalogram (EEG) decoding plays a critical role in brain–computer interfaces but remains challenging due to large inter-subject variability and limited training data.
The final, formatted version of the article will be published soon. Motor imagery (MI) based electroencephalography (EEG) classification is central to brain–computer interface (BCI) research but ...
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 trade-off ...
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 ...
Electroencephalography (EEG) is a fascinating noninvasive technique that measures and records the brain's electrical activity. It detects small electrical signals produced when neurons in the brain ...
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