國家衛生研究院 NHRI:Item 3990099045/10216

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Title:	A Sliced Inverse Regression (SIR) decoding the forelimb movement from neuronal spikes in the rat motor cortex
Authors:	Yang, SH;Chen, YY;Lin, SH;Liao, LD;Lu, HH;Wang, CF;Chen, PC;Lo, YC;Phan, TD;Chao, HY;Lin, HC;Lai, HY;Huang, WC
Contributors:	Institute of Biomedical Engineering and Nanomedicine
Abstract:	A Sliced Inverse Regression (SIR) decoding the forelimb movement from neuronal spikes in the rat motor cortex Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE), and neural networks (NN), are effective in predicting movement kinematics, including movement direction, speed and trajectory but usually require a large number of neurons to achieve desirable performance. This study proposed a novel decoding algorithm even with signals obtained from a smaller numbers of neurons. We adopted sliced inverse regression (SIR) to predict forelimb movement from single-unit activities recorded in the rat primary motor (M1) cortex in a water-reward lever-pressing task. SIR performed weighted principal component analysis (PCA) to achieve effective dimension reduction for nonlinear regression. To demonstrate the decoding performance, SIR was compared to PVA, OLE, and NN. Furthermore, PCA and sequential feature selection (SFS) which are popular feature selection techniques were implemented for comparison of feature selection effectiveness. Among SIR, PVA, OLE, PCA, SFS, and NN decoding methods, the trajectories predicted by SIR (with a root mean square error, RMSE, of 8.47 +/- 1.32 mm) was closer to the actual trajectories compared with those predicted by PVA (30.41 +/- 11.73 mm), OLE (20.17 +/- 6.43 mm), PCA (19.13 +/- 0.75 mm), SFS (22.75 +/- 2.01 mm), and NN (16.75 +/- 2.02 mm). The superiority of SIR was most obvious when the sample size of neurons was small. We concluded that SIR sorted the input data to obtain the effective transform matrices for movement prediction, making it a robust decoding method for conditions with sparse neuronal information.
Date:	2016-12-09
Relation:	Frontiers in Neuroscience. 2016 Dec 09;10:Article number 556.
Link to:	http://dx.doi.org/10.3389/fnins.2016.00556
JIF/Ranking 2023:	http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1662-453X&DestApp=IC2JCR
Cited Times(WOS):	https://www.webofscience.com/wos/woscc/full-record/WOS:000389778800001
Cited Times(Scopus):	http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85009811788
Appears in Collections:	[Lun-De Liao] Periodical Articles

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