Block design enhances classification of 3D reach targets from electroencephalographic signals

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.

[1]  P. Nunez.  Toward a quantitative description of large-scale neocortical dynamic function and EEG , 2000, Behavioral and Brain Sciences.

[2]  R. B. Reilly,et al.  FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection , 2010, Journal of Neuroscience Methods.

[3]  E. Vaadia,et al.  The Neuronal Basis of Long-Term Sensorimotor Learning , 2011, The Journal of Neuroscience.

[4]  Leslie G. Ungerleider,et al.  Functional MRI evidence for adult motor cortex plasticity during motor skill learning , 1995, Nature.

[5]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Leslie G. Ungerleider,et al.  The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Christa Neuper,et al.  An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate , 2004, IEEE Transactions on Biomedical Engineering.

[8]  R. Shadmehr,et al.  Neural correlates of motor memory consolidation. , 1997, Science.

[9]  A. Leuthold,et al.  Beta-Band Activity during Motor Planning Reflects Response Uncertainty , 2010, The Journal of Neuroscience.

[10]  I. Toni,et al.  Distinct roles for alpha- and beta-band oscillations during mental simulation of goal-directed actions. , 2014, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[11]  Trent J. Bradberry,et al.  Reconstructing Three-Dimensional Hand Movements from Noninvasive Electroencephalographic Signals , 2010, The Journal of Neuroscience.

[12]  Towards Decoding 3 D Finger Trajectories from EEG , 2011 .

[13]  Gernot R. Müller-Putz,et al.  Using a Noninvasive Decoding Method to Classify Rhythmic Movement Imaginations of the Arm in Two Planes , 2015, IEEE Transactions on Biomedical Engineering.

[14]  Koby Crammer,et al.  On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines , 2002, J. Mach. Learn. Res..

[15]  S. Scott,et al.  Changes in motor cortex activity during reaching movements with similar hand paths but different arm postures. , 1995, Journal of neurophysiology.

[16]  Keng Peng Tee,et al.  Multi-class EEG classification of voluntary hand movement directions , 2013, Journal of neural engineering.

[17]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[18]  José Luis Contreras-Vidal,et al.  Reconstructing hand kinematics during reach to grasp movements from electroencephalographic signals , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  H. Poizner,et al.  Predicting Reaching Targets from Human EEG , 2008, IEEE Signal Processing Magazine.

[20]  Andrew Y. Paek,et al.  Global cortical activity predicts shape of hand during grasping , 2015, Front. Neurosci..

[21]  D. Hoffman,et al.  Muscle and movement representations in the primary motor cortex. , 1999, Science.

[22]  Xiangyang Zhu,et al.  A novel calibration and task guidance framework for motor imagery BCI via a tendon vibration induced sensation with kinesthesia illusion , 2015, Journal of neural engineering.

[23]  J. Millán,et al.  Single trial prediction of self-paced reaching directions from EEG signals , 2014, Front. Neurosci..

[24]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[25]  Hans Helmut Kornhuber,et al.  Human Freedom, Reasoned Will, and the Brain: The Bereitschaftspotential Story , 2003 .

[26]  J. Mazziotta,et al.  Mapping motor representations with positron emission tomography , 1994, Nature.

[27]  E. Bizzi,et al.  Consolidation in human motor memory , 1996, Nature.

[28]  C. Mehring,et al.  Encoding of Movement Direction in Different Frequency Ranges of Motor Cortical Local Field Potentials , 2005, The Journal of Neuroscience.

[29]  A B Schwartz,et al.  Motor cortical representation of speed and direction during reaching. , 1999, Journal of neurophysiology.

[30]  J. Krakauer,et al.  Consolidation of motor memory , 2006, Trends in Neurosciences.

[31]  A. P. Georgopoulos,et al.  Neuronal population coding of movement direction. , 1986, Science.

[32]  D. Farina,et al.  Detection of movement intention from single-trial movement-related cortical potentials , 2011, Journal of neural engineering.

[33]  Heba M. Lakany,et al.  Understanding intention of movement from electroencephalograms , 2007, Expert Syst. J. Knowl. Eng..

[34]  Andrew Y. Paek,et al.  Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography , 2014, Front. Neuroeng..

[35]  C. Nam,et al.  Movement imagery-related lateralization of event-related (de)synchronization (ERD/ERS): Motor-imagery duration effects , 2011, Clinical Neurophysiology.

[36]  M. Hallett,et al.  Prediction of human voluntary movement before it occurs , 2011, Clinical Neurophysiology.

[37]  C. Braun,et al.  Hand Movement Direction Decoded from MEG and EEG , 2008, The Journal of Neuroscience.

[38]  S. Blakemore,et al.  Motor activation prior to observation of a predicted movement , 2004, Nature Neuroscience.

[39]  Moritz Grosse-Wentrup,et al.  Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction , 2008, IEEE Transactions on Biomedical Engineering.

[40]  Xuedong Chen,et al.  Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control , 2009, Journal of neural engineering.