title: Block design enhances classification of 3D reach targets from electroencephalographic signals.
abstract: To date, decoding accuracy of actual or imagined pointing movements to targets in 3D space from electroencephalographic (EEG) signals has remained modest. The reason may pertain to the fact that these movements activate essentially the same neural networks. In this study, we aimed at testing whether repetitive pointing movements to each of the targets promotes the development of segregated neural patterns, resulting in enhanced decoding accuracy. Six human subjects generated slow or fast repetitive pointing movements with their right dominant arm to one of five targets distributed in 3D space, followed by repetitive imagery of movements to the same target or to a different target. Nine naive subjects generated both repetitive and non-repetitive slow actual movements to each of the five targets to test the effect of block design on decoding accuracy. In order to assure that base line drift and low frequency motion artifacts do not contaminate the data, the data were high-pass filtered in 4-30Hz, leaving out the delta and gamma band. For the repetitive trials, the model decoded target location with 81% accuracy, which is significantly higher than chance level. The average decoding rate of target location was only 30% for the non-repetitive trials, which is not significantly different than chance level. A subset of electrodes, mainly over the contralateral sensorimotor areas, was found to provide most of the discriminative features for all tested conditions. Time proximity between trained and tested blocks was found to enhance decoding accuracy of target location both by target non-specific and specific mechanisms. Our findings suggest that movement repetition promotes the development of distinct neural patterns, presumably by the formation of target-specific kinesthetic memory.
authors.affiliation: Faculty of Electrical, Electronics and Communication Engineering, Holon Institute of Technology, Holon 5810201, Israel. Electronic address: ronens@hit.ac.il.
authors.forename: Ronen
authors.initials: R
authors.lastname: Sosnik
authors.affiliation: Electrical and Computer Engineering, The University of Texas, Austin, TX 78705, USA.
authors.forename: Vijay Aditya
authors.initials: VA
authors.lastname: Tadipatri
authors.affiliation: Electrical and Computer Engineering, The University of Texas, Austin, TX 78705, USA.
authors.forename: Ahmed H
authors.initials: AH
authors.lastname: Tewfik
authors.affiliation: Brain Sciences Center, Veterans Affairs Health Care Center, Minneapolis, MN 55417, USA; Department of Neuroscience, University of Minnesota, Minneapolis 55455, USA.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Pellizzer
mesh_terms: D000328:Adult; D001132:Arm / Q000502:physiology*; D001696:Biomechanical Phenomena; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D004576:Electromyography; D004585:Electrooculography; D007839:Functional Laterality; D006801:Humans; D007092:Imagination / Q000502:physiology*; D007858:Learning / Q000502:physiology; D008297:Male; D008568:Memory / Q000502:physiology; D008875:Middle Aged; D009043:Motor Activity / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted
keywords: 3D reach movements; EEG; classification of targets; motor areas; motor imagery