Jo-anne Ting, Aaron D'souza, Kenji Yamamoto, Toshinori Yoshioka, Donna Hoffman, Shinji Kakei, Lauren Sergio, John Kalaska, Mitsuo Kawato
An increasing number of projects in neuroscience requires the sta- tistical analysis of high dimensional data sets, as, for instance, in predicting behavior from neural ﬁring or in operating artiﬁcial de- vices from brain recordings in brain-machine interfaces. Linear analysis techniques remain prevalent in such cases, but classical linear regression approaches are often numerically too fragile in high dimensions. In this paper, we address the question of whether EMG data collected from arm movements of monkeys can be faith- fully reconstructed with linear approaches from neural activity in primary motor cortex (M1). To achieve robust data analysis, we develop a full Bayesian approach to linear regression that auto- matically detects and excludes irrelevant features in the data, reg- ularizing against overﬁtting. In comparison with ordinary least squares, stepwise regression, partial least squares, LASSO regres- sion and a brute force combinatorial search for the most predictive input features in the data, we demonstrate that the new Bayesian method oﬀers a superior mixture of characteristics in terms of reg- ularization against overﬁtting, computational eﬃciency and ease of use, demonstrating its potential as a drop-in replacement for other linear regression techniques. As neuroscientiﬁc results, our anal- yses demonstrate that EMG data can be well predicted from M1 neurons, further opening the path for possible real-time interfaces between brains and machines.