Associative Plasticity Induced by a Brain-Computer Interface Based on Movement-Related Cortical Potentials

This chapter presents the basic concepts of a brain-computer-interface (BCI) designed for neuromodulation that is based on known theories of memory storage and learning. Initially, an overview is provided of the control signal, the movement-related cortical potential (MRCP), its advantages over other signal modalities, and its neural generators. This is followed by a detailed account of factors that affect the MRCP morphology such as task parameters, shifts in user attention and plasticity, and insights into algorithm design for both detection and classification in an online self-paced BCI. Finally, the applications of this type of associative BCI to more complex tasks such as human gait and in the clinical environment with patients are presented.