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Instantaneous estimation of motor cortical neural encoding for online brain-machine interfaces.
J Neural Eng. 2010 Oct; 7(5):056010.JN

Abstract

Recently, the authors published a sequential decoding algorithm for motor brain-machine interfaces (BMIs) that infers movement directly from spike trains and produces a new kinematic output every time an observation of neural activity is present at its input. Such a methodology also needs a special instantaneous neuronal encoding model to relate instantaneous kinematics to every neural spike activity. This requirement is unlike the tuning methods commonly used in computational neuroscience, which are based on time windows of neural and kinematic data. This paper develops a novel, online, encoding model that uses the instantaneous kinematic variables (position, velocity and acceleration in 2D or 3D space) to estimate the mean value of an inhomogeneous Poisson model. During BMI decoding the mapping from neural spikes to kinematics is one to one and easy to implement by simply reading the spike times directly. Due to the high temporal resolution of the encoding, the delay between motor cortex neurons and kinematics needs to be estimated in the encoding stage. Mutual information is employed to select the optimal time index defined as the lag for which the spike event is maximally informative with respect to the kinematics. We extensively compare the windowed tuning models with the proposed method. The big difference between them resides in the high firing rate portion of the tuning curve, which is rather important for BMI-decoding performance. This paper shows that implementing such an instantaneous tuning model in sequential Monte Carlo point process estimation based on spike timing provides statistically better kinematic reconstructions than the linear and exponential spike-tuning models.

Authors+Show Affiliations

Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, People's Republic of China. wangyw@cnel.ufl.eduNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

20841635

Citation

Wang, Yiwen, and Jose C. Principe. "Instantaneous Estimation of Motor Cortical Neural Encoding for Online Brain-machine Interfaces." Journal of Neural Engineering, vol. 7, no. 5, 2010, p. 056010.
Wang Y, Principe JC. Instantaneous estimation of motor cortical neural encoding for online brain-machine interfaces. J Neural Eng. 2010;7(5):056010.
Wang, Y., & Principe, J. C. (2010). Instantaneous estimation of motor cortical neural encoding for online brain-machine interfaces. Journal of Neural Engineering, 7(5), 056010. https://doi.org/10.1088/1741-2560/7/5/056010
Wang Y, Principe JC. Instantaneous Estimation of Motor Cortical Neural Encoding for Online Brain-machine Interfaces. J Neural Eng. 2010;7(5):056010. PubMed PMID: 20841635.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Instantaneous estimation of motor cortical neural encoding for online brain-machine interfaces. AU - Wang,Yiwen, AU - Principe,Jose C, Y1 - 2010/09/14/ PY - 2010/9/16/entrez PY - 2010/9/16/pubmed PY - 2011/8/13/medline SP - 056010 EP - 056010 JF - Journal of neural engineering JO - J Neural Eng VL - 7 IS - 5 N2 - Recently, the authors published a sequential decoding algorithm for motor brain-machine interfaces (BMIs) that infers movement directly from spike trains and produces a new kinematic output every time an observation of neural activity is present at its input. Such a methodology also needs a special instantaneous neuronal encoding model to relate instantaneous kinematics to every neural spike activity. This requirement is unlike the tuning methods commonly used in computational neuroscience, which are based on time windows of neural and kinematic data. This paper develops a novel, online, encoding model that uses the instantaneous kinematic variables (position, velocity and acceleration in 2D or 3D space) to estimate the mean value of an inhomogeneous Poisson model. During BMI decoding the mapping from neural spikes to kinematics is one to one and easy to implement by simply reading the spike times directly. Due to the high temporal resolution of the encoding, the delay between motor cortex neurons and kinematics needs to be estimated in the encoding stage. Mutual information is employed to select the optimal time index defined as the lag for which the spike event is maximally informative with respect to the kinematics. We extensively compare the windowed tuning models with the proposed method. The big difference between them resides in the high firing rate portion of the tuning curve, which is rather important for BMI-decoding performance. This paper shows that implementing such an instantaneous tuning model in sequential Monte Carlo point process estimation based on spike timing provides statistically better kinematic reconstructions than the linear and exponential spike-tuning models. SN - 1741-2552 UR - https://www.unboundmedicine.com/medline/citation/20841635/Instantaneous_estimation_of_motor_cortical_neural_encoding_for_online_brain_machine_interfaces_ L2 - https://doi.org/10.1088/1741-2560/7/5/056010 DB - PRIME DP - Unbound Medicine ER -