Contributions
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Discriminative Manifold Learning Based Detection of Movement-Related Cortical Potentials
The detection of voluntary motor intention from EEG has been applied to closed-loop brain–computer interfacing (BCI). The movement-related cortical potential (MRCP) is a low frequency component of the EEG signal, which represents movement intention, preparation, and execution. In this study, we aim at detecting MRCPs from single-trial EEG traces. For this purpose, we propose a detector based on a discriminant manifold learning method, called locality sensitive discriminant analysis (LSDA), and we test it in both online and offline experiments with executed and imagined movements.
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Endogenous Sensory Discrimination and Selection by a Fast Brain Switch for a High Transfer Rate Brain-Computer Interface
In this study, we present a novel multi-class brain-computer interface (BCI) system for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input.