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Summary: Brief Communications
Reconstructing Three-Dimensional Hand Movements from
Noninvasive Electroencephalographic Signals
Trent J. Bradberry,1 Rodolphe J. Gentili,2,3 and Jose´ L. Contreras-Vidal1,2,3
1Fischell Department of Bioengineering, 2Department of Kinesiology, and 3Graduate Program in Neuroscience and Cognitive Science, University of
Maryland, College Park, Maryland 20742
It is generally thought that the signal-to-noise ratio, the bandwidth, and the information content of neural data acquired via noninvasive scalp
electroencephalography(EEG)areinsufficienttoextractdetailedinformationaboutnatural,multijointmovementsoftheupperlimb.Here,we
challenge this assumption by continuously decoding three-dimensional (3D) hand velocity from neural data acquired from the scalp with
55-channelEEGduringa3Dcenter-outreachingtask.Topreserveecologicalvalidity,fivesubjectsself-initiatedreachesandself-selectedtargets.
Eye movements were controlled so they would not confound the interpretation of the results. With only 34 sensors, the correlation between
measuredandreconstructedvelocityprofilescomparedreasonablywelltothatreportedbystudiesthatdecodedhandkinematicsfromneural
activityacquiredintracranially.WesubsequentlyexaminedtheindividualcontributionsofEEGsensorstodecodingtofindsubstantialinvolve-
ment of scalp areas over the sensorimotor cortex contralateral to the reaching hand. Using standardized low-resolution brain electromagnetic
tomography (sLORETA), we identified distributed current density sources related to hand velocity in the contralateral precentral gyrus, post-
central gyrus, and inferior parietal lobule. Furthermore, we discovered that movement variability negatively correlated with decoding accuracy, a
findingtoconsiderduringthedevelopmentofbraincomputerinterfacesystems.Overall,theabilitytocontinuouslydecode3Dhandvelocityfrom
EEG during natural, center-out reaching holds promise for the furtherance of noninvasive neuromotor prostheses for movement-impaired
individuals.
Introduction
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