Brain-machine interfaces: past, present and future
PulseAugur coverage of Brain-machine interfaces: past, present and future — every cluster mentioning Brain-machine interfaces: past, present and future across labs, papers, and developer communities, ranked by signal.
-
New attention-based model decodes movement parameters from EEG signals
Researchers have developed three regression models to decode multiple movement parameters from electroencephalography (EEG) signals for brain-machine interfaces (BMIs). The attention-based regressor demonstrated the hig…
-
New SSCDL framework enhances Brain-Machine Interface generalization
Researchers have developed a new framework called Self-Supervised Consistency enhanced Disentangled Learning (SSCDL) to improve the generalization capabilities of Brain-Machine Interfaces (BMIs). This approach addresses…
-
New EEGForceFusion framework enhances grasp force decoding for brain-machine interfaces
Researchers have developed a novel brain-machine interface framework, EEGForceFusion, designed to improve the decoding of grasp force from electroencephalography (EEG) signals. This hybrid approach combines continuous a…
-
New framework tackles neural drift for stable long-term Brain-Machine Interfaces
Researchers have developed a new framework called Uncertainty-guided Self-paced Cycling (UnSPC) to address the challenge of neural drift in long-term Brain-Machine Interfaces (BMIs). This framework combines domain adapt…