title: Sensorimotor rhythm-based brain-computer interface training: the impact on motor cortical responsiveness.
abstract: The main purpose of electroencephalography (EEG)-based brain-computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22-29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.
authors: Neurolelectrical Imaging and BCI Laboratory, IRCCS Fondazione Santa Lucia, Rome, Italy.
authors: F
authors: F
authors: Pichiorri
authors: F
authors: F
authors: De Vico Fallani
authors: F
authors: F
authors: Cincotti
authors: F
authors: F
authors: Babiloni
authors: M
authors: M
authors: Molinari
authors: S C
authors: SC
authors: Kleih
authors: C
authors: C
authors: Neuper
authors: A
authors: A
authors: Kübler
authors: D
authors: D
authors: Mattia
mesh_terms: D000328:Adult; D000465:Algorithms; D001683:Biological Clocks / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology; D005073:Evoked Potentials, Somatosensory / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D007858:Learning / Q000502:physiology; D008297:Male; D009044:Motor Cortex / Q000502:physiology*; D014584:User-Computer Interface