Design and implementation of a human ECoG simulator for testing brain-machine interfaces

This paper presents the design and implementation of a signal simulator that emulates event-related human electrocorticographic (ECoG) signals. This real-time simulator renders a representative model of human ECoG encompassing prominent physiological modulation in the time domain (e.g., event-related potentials, or ERPs) and the frequency domain (e.g., alpha/mu, beta, and high gamma band). The simulated signals were generated in a MATLAB SIMULINK framework and output through a National Instruments PCI card for recording by a standard research-grade ECoG amplifier system. Trial-averaged event-related spectrograms computed offline from simulated signals exhibit characteristics similar to those of experimental human ECoG recordings. The presented simulator can serve as a useful tool for testing real-time brain-machine interface (BMI) applications. It can also serve as a potential framework for future implementation of neuronal models for generation of extracellular field potentials.

[1]  S. Acharya,et al.  Toward Electrocorticographic Control of a Dexterous Upper Limb Prosthesis: Building Brain-Machine Interfaces , 2012, IEEE Pulse.

[2]  D. Moran,et al.  Cortical Adaptation to a Chronic Micro-Electrocorticographic Brain Computer Interface , 2013, The Journal of Neuroscience.

[3]  Gerwin Schalk,et al.  A brain–computer interface using electrocorticographic signals in humans , 2004, Journal of neural engineering.

[4]  Piotr J. Franaszczuk,et al.  Phase-dependent stimulation effects on bursting activity in a neural network cortical simulation , 2009, Epilepsy Research.

[5]  H. Yokoi,et al.  Electrocorticographic control of a prosthetic arm in paralyzed patients , 2012, Annals of neurology.

[6]  J. A. Wilson,et al.  Two-dimensional movement control using electrocorticographic signals in humans , 2008, Journal of neural engineering.

[7]  Michael L. Boninger,et al.  Toward Synergy-Based Brain-Machine Interfaces , 2011, IEEE Transactions on Information Technology in Biomedicine.

[8]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[9]  N. Thakor,et al.  Electrocorticographic amplitude predicts finger positions during slow grasping motions of the hand , 2010, Journal of neural engineering.

[10]  J. Carmena,et al.  Emergence of a Stable Cortical Map for Neuroprosthetic Control , 2009, PLoS biology.

[11]  H. Yokoi,et al.  Real-time control of a prosthetic hand using human electrocorticography signals. , 2011, Journal of neurosurgery.

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

[13]  G. Schalk,et al.  Brain-Computer Interfaces Using Electrocorticographic Signals , 2011, IEEE Reviews in Biomedical Engineering.

[14]  C. Koch,et al.  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.

[15]  Josef Parvizi,et al.  Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas , 2013, Journal of neural engineering.

[16]  Robin C. Ashmore,et al.  An Electrocorticographic Brain Interface in an Individual with Tetraplegia , 2013, PloS one.

[17]  N. Crone,et al.  Cortical γ responses: searching high and low. , 2011, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.