A tensor-product-kernel framework for multiscale neural activity decoding and control.
Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation.
Philips Research North America, Briarcliff Manor, NY 10510, USA.
Lin
L
Li
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
Austin J
AJ
Brockmeier
Joint Program in Biomedical Engineering, NYU Polytechnic School of Engineering and SUNY Downstate, Brooklyn, NY 11203, USA.
John S
JS
Choi
Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, Joint Program in Biomedical Engineering, NYU Polytechnic School of Engineering and SUNY Downstate, Robert F. Furchgott Center for Neural & Behavioral Science, Brooklyn, NY 11203, USA.
Joseph T
JT
Francis
Department of Biomedical Engineering, Department of Neuroscience, Miami Project to Cure Paralysis, University of Miami, Coral Gables, FL 33146, USA.
Justin C
JC
Sanchez
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
José C
JC
Príncipe
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001921:Brain / Q000166:cytology*; D062207:Brain-Computer Interfaces; D003198:Computer Simulation; D004558:Electric Stimulation; D005071:Evoked Potentials / Q000502:physiology; D005260:Female; D005385:Fingers / Q000294:innervation; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D051381:Rats; D020318:Rats, Long-Evans; D012815:Signal Processing, Computer-Assisted; D014110:Touch