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| Record Number | 10264 |
| Reference Type | Journal Article |
| Author(s) | Ting, J. A.;D'Souza, A.;Yamamoto, K.;Yoshioka, T.;Hoffman, D.;Kakei, S.;Sergio, L.;Kalaska, J.;Kawato, M.;Strick, P.;Schaal, S. |
| Year | 2008 |
| Title | Variational Bayesian least squares: an application to brain-machine interface data |
| Journal/Conference/Book Title | Neural Netw |
| Keywords | motor control, computational neuroscience, emg reconstruction, motor cortex, brain machine interfaces, bayesian least squares |
Abstract |
An increasing number of projects in neuroscience require statistical analysis of high-dimensional data, as, for instance, in the prediction of behavior from neural firing or in the operation of artificial devices from brain recordings in brain-machine interfaces. Although prevalent, classical linear analysis techniques are often numerically fragile in high dimensions due to irrelevant, redundant, and noisy information. We developed a robust Bayesian linear regression algorithm that automatically detects relevant features and excludes irrelevant ones, all in a computationally efficient manner. In comparison with standard linear methods, the new Bayesian method regularizes against overfitting, is computationally efficient (unlike previously proposed variational linear regression methods, is suitable for data sets with large numbers of samples and a very high number of input dimensions) and is easy to use, thus demonstrating its potential as a drop-in replacement for other linear regression techniques. We evaluate our technique on synthetic data sets and on several neurophysiological data sets. For these neurophysiological data sets we address the question of whether EMG data collected from arm movements of monkeys can be faithfully reconstructed from neural activity in motor cortices. Results demonstrate the success of our newly developed method, in comparison with other approaches in the literature, and, from the neurophysiological point of view, confirms recent findings on the organization of the motor cortex. Finally, an incremental, real-time version of our algorithm demonstrates the suitability of our approach for real-time interfaces between brains and machines.
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| Notes | clmc
Ting, Jo-Anne
D'Souza, Aaron
Yamamoto, Kenji
Yoshioka, Toshinori
Hoffman, Donna
Kakei, Shinji
Sergio, Lauren
Kalaska, John
Kawato, Mitsuo
Strick, Peter
Schaal, Stefan
United States
Neural networks : the official journal of the International Neural Network Society
Neural Netw. 2008 Oct;21(8):1112-31. Epub 2008 Jun 27. |
| URL(s) | http://www-clmc.usc.edu/publications/t/ting-NN2008.pdf
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| Volume | 21 |
| Number | 8 |
| Pages | 1112-31 |
| Edition | 2008/08/02 |
| Date | Oct |
| Short Title | Variational Bayesian least squares: an application to brain-machine interface data |
| ISBN/ISSN | 0893-6080 (Print) |
| Custom 2 | 2622433 |
| Accession Number | 18672345 |
| Address | University of Southern California, Los Angeles, CA 90089, USA. joanneti@usc.edu |
| Electronic Resource Number | S0893-6080(08)00133-0 [pii]
10.1016/j.neunet.2008.06.012 |
| Language | eng |
| Papers are available as Adobe PDF ".pdf" files. Adobe Reader is available for free for all computer platforms.
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