Abstract
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.
Highlights
In order to improve daily life activities for paralyzed patients, the establishment of a non-muscular communication interface between brain neurons and machines has rapidly developed over the last two decades (Schwartz, 1993, 1994; Donoghue, 2002; Schwartz et al, 2006; Velliste et al, 2008)
With assistance from stable generated brain-derived control signals incorporated with prosthetic devices and motor functions, paralyzed patients possibly regain their ability to move a computer cursor (Kennedy et al, 2000; Hochberg et al, 2006; Gilja et al, 2015), control an anthropomorphic prosthetic arm (Wodlinger et al, 2015), and drive a prosthetic device (Hochberg et al, 2012; Collinger et al, 2013) through a brain-machine interface (BMI)
principal component analysis (PCA) was developed as dimension reduction of feature space, it could be considered as a way to select features from principle components
Summary
In order to improve daily life activities for paralyzed patients, the establishment of a non-muscular communication interface between brain neurons and machines has rapidly developed over the last two decades (Schwartz, 1993, 1994; Donoghue, 2002; Schwartz et al, 2006; Velliste et al, 2008). For brain-derived control signals, neural decoding is an indispensable technique that translates neuronal activities to physical states, such as the position of a foraging rat (Brown et al, 1998), arm movement (Ashe and Georgopoulos, 1994), movement speed (Moran and Schwartz, 1999), hand position (Paninski et al, 2004), and joint angular velocity (Reina et al, 2001). A previous study showed that PVA decoding could expose the visuomotor coordinate transformations between visual and motor information by processing masses of neuronal activities recorded from relative brain regions (Takeda and Funahashi, 2004; Watanabe et al, 2009). Requiring large numbers of neurons with a temporal solution of 10–100 ms, PVA and OLE studies successfully predicted the kinematic parameters of a primate arm movement (Schwartz et al, 2001; Takeda and Funahashi, 2004; Watanabe et al, 2009)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
