Reconstructing for joint angles on the shoulder and elbow from non-invasive electroencephalographic signals through electromyography

In this study, first the cortical activities over 2240 vertexes on the brain were estimated from 64 channels electroencephalography (EEG) signals using the Hierarchical Bayesian estimation while 5 subjects did continuous arm reaching movements. From the estimated cortical activities, a sparse linear regression method selected only useful features in reconstructing the electromyography (EMG) signals and estimated the EMG signals of 9 arm muscles. Then, a modular artificial neural network was used to estimate four joint angles from the estimated EMG signals of 9 muscles: one for movement control and the other for posture control. The estimated joint angles using this method have the correlation coefficient (CC) of 0.807 (±0.10) and the normalized root-mean-square error (nRMSE) of 0.176 (±0.29) with the actual joint angles.

[1]  Makoto Sato,et al.  Stiffness Teaching and Motion Assist System Using Functional Electrical Stimulation and Electromyogram Signals , 2004, J. Robotics Mechatronics.

[2]  J J Riera,et al.  Evaluation of inverse methods and head models for EEG source localization using a human skull phantom , 2001, Physics in medicine and biology.

[3]  J D Simeral,et al.  Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia , 2011, Journal of neural engineering.

[4]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[5]  R. Stein,et al.  Determination of the frequency response of isometric soleus muscle in the cat using random nerve stimulation , 1973, The Journal of physiology.

[6]  Mitsuo Kawato,et al.  Sparse linear regression for reconstructing muscle activity from human cortical fMRI , 2008, NeuroImage.

[7]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

[8]  Yasuharu Koike,et al.  Estimation of Arm Posture in 3D-Space from Surface EMG Signals Using a Neural Network Model (Special Issue on Neurocomputing) , 1994 .

[9]  Jose C. Principe,et al.  Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics , 2008, Journal of Neuroscience Methods.

[10]  S. Meagher.  Instant neural control of a movement signal , 2002 .

[11]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[12]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[13]  Yasuharu Koike,et al.  Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model , 1995, Biological Cybernetics.

[14]  Masa-aki Sato,et al.  Online Model Selection Based on the Variational Bayes , 2001, Neural Computation.

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

[16]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[17]  E. Fetz,et al.  Direct control of paralyzed muscles by cortical neurons , 2008, Nature.

[18]  J. Wolpaw,et al.  Decoding two-dimensional movement trajectories using electrocorticographic signals in humans , 2007, Journal of neural engineering.

[19]  Christopher R. Stambaugh,et al.  Simultaneous encoding of tactile information by three primate cortical areas , 1998, Nature Neuroscience.

[20]  Masa-aki Sato,et al.  Artifact Removal Using Simultaneous Current Estimation of Noise and Cortical Sources , 2008, ICONIP.

[21]  Y. Koike.  Virtual trajectories predicted from surface EMG signal , 1993 .

[22]  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.

[23]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[24]  Dawn M. Taylor,et al.  Direct Cortical Control of 3D Neuroprosthetic Devices , 2002, Science.

[25]  E. V. Jones,et al.  Functional electrical stimulation in tetraplegic patients to restore hand function. , 2002, Journal of long-term effects of medical implants.

[26]  G. McCarthy,et al.  Functional organization of human supplementary motor cortex studied by electrical stimulation , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[27]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[28]  Yasuharu Koike,et al.  Prediction of arm trajectory from a small number of neuron activities in the primary motor cortex , 2006, Neuroscience Research.

[29]  J. Daube,et al.  Muscles Alive , 1981, Neurology.

[30]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[31]  Masa-aki Sato,et al.  Reconstruction of two-dimensional movement trajectories from selected magnetoencephalography cortical currents by combined sparse Bayesian methods , 2011, NeuroImage.

[32]  Masa-aki Sato,et al.  Hierarchical Bayesian estimation for MEG inverse problem , 2004, NeuroImage.

[33]  Geoffrey E. Hinton,et al.  An Efficient Learning Procedure for Deep Boltzmann Machines , 2012, Neural Computation.

[34]  Shamim Nemati,et al.  Biomimetic Brain Machine Interfaces for the Control of Movement , 2007, The Journal of Neuroscience.

[35]  Wei Wu,et al.  Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter , 2006, Neural Computation.

[36]  Shiro Usui,et al.  Improved kick out learning algorithm with delta-bar-delta-bar rule , 1993, IEEE International Conference on Neural Networks.

[37]  Hagai Attias,et al.  Inferring Parameters and Structure of Latent Variable Models by Variational Bayes , 1999, UAI.

[38]  Miguel A. L. Nicolelis,et al.  Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex , 1999, Nature Neuroscience.

[39]  Jerald D. Kralik,et al.  Real-time prediction of hand trajectory by ensembles of cortical neurons in primates , 2000, Nature.

[40]  H. Dinse,et al.  A repetitive intracortical microstimulation pattern induces long-lasting synaptic depression in brain slices of the rat primary somatosensory cortex , 2000, Experimental Brain Research.

[41]  M. M. Morrow,et al.  Prediction of muscle activity by populations of sequentially recorded primary motor cortex neurons. , 2003, Journal of neurophysiology.

[42]  Masa-aki Sato,et al.  A hierarchical Bayesian method to resolve an inverse problem of MEG contaminated with eye movement artifacts , 2009, NeuroImage.

[43]  Matthew Fellows,et al.  Statistical encoding model for a primary motor cortical brain-machine interface , 2005, IEEE Transactions on Biomedical Engineering.