title: Movement decoding from noninvasive neural signals.
abstract: It is generally assumed that noninvasively-acquired neural signals contain an insufficient level of information for decoding or reconstructing detailed kinematics of natural, multi-joint limb movements and hand gestures. Here, we review recent findings from our laboratory at the University of Maryland showing that noninvasive scalp electroencephalography (EEG) or magnetoencephalography (MEG) can be used to continuously decode the kinematics of 2D 'center-out' drawing, unconstrained 3D 'center-out' reaching and 3D finger gesturing. These findings suggest that these 'far-field', extra-cranial neural signals contain rich information about the neural representation of movement at the macroscale, and thus these neural representations provide alternative methods for developing noninvasive brain-machine interfaces with wide-ranging clinical relevance and for understanding functional and pathological brain states at various stages of development and aging.
authors.affiliation: Department of Kinesiology, and the Graduate Programs in Bioengineering and Neuroscience & Cognitive Science, University of Maryland, College Park, USA. pepeum@umd.edu
authors.forename: Jose L
authors.initials: JL
authors.lastname: Contreras-Vidal
authors.forename: Trent J
authors.initials: TJ
authors.lastname: Bradberry
authors.forename: Harshavardhan
authors.initials: H
authors.lastname: Agashe
mesh_terms: D000375:Aging; D000818:Animals; D001696:Biomechanical Phenomena; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D000882:Haplorhini; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods; D008396:Maryland; D008959:Models, Neurological; D009039:Motion Perception; D009415:Nerve Net; D009474:Neurons / Q000378:metabolism*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted