Clustering of Preferred Directions During Brain-Computer Interface Usage
Chiou, Jeffrey
(2017)
Clustering of Preferred Directions During Brain-Computer Interface Usage.
Master's Thesis, University of Pittsburgh.
(Unpublished)
![[img]](http://d-scholarship.pitt.edu/29723/2.hassmallThumbnailVersion/Chiou%20Clustering%20of%20Preferred%20Directions%20During%20Brain-Computer%20Interface%20Usage.pdf)  Preview |
|
PDF (Clustering of Preferred Directions During Brain-Computer Interface Usage)
Primary Text
Download (4MB)
| Preview
|
Abstract
Brain-computer interfaces (BCIs) are proving to be viable clinical interventions for sufferers of amyotrophic lateral sclerosis, amputations, and spinal cord injuries. To improve the viability of BCIs, it will help to have a thorough understanding of how the brain controls them. Neural activity during usage of certain BCIs behaves in a surprising and seemingly counterintuitive manner – the preferred directions (PDs) of neurons cluster together. We trained monkeys to reach to targets in a center-out task either using their arm or a BCI. We found that neurons’ PDs cluster similarly during training of the BCI decoder and usage of the BCI, but remain relatively unclustered when the monkeys use their arms. Modulation depths increase upon usage of the BCI, and narrowness of tuning tends to either increase or decrease rather than staying the same. In addition, the cluster direction can be predicted from per-target performance. A model where two neurons’ PDs approach one another reveals how much modulation depths have to increase to maintain controllability. This thesis concludes with considerations of why this clustering might occur, and whether or not it benefits BCI control.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
18 January 2017 |
| Date Type: |
Publication |
| Defense Date: |
2 September 2016 |
| Approval Date: |
18 January 2017 |
| Submission Date: |
13 September 2016 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
55 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Medicine > Neurobiology |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
brain-computer interface, brain-machine interface, BCI, BMI, preferred direction, neural tuning, modulation depth, von Mises, clustering, population, controllability, narrowness, half-height width, tortuosity, circular-linear regression, Kalman filter, straightness, angular dispersion, Rao, circular variance, neuron, neuroscience, motor cortex, sensorimotor, feedback, tuning properties |
| References: |
Amirikian, B., and Georgopulos, A.P. (2000). Directional tuning profiles of motor cortical cells. Neurosci. Res. 36, 73-79.
Batschelet, E. (1981). Circular statistics in biology. Acad. PRESS 111 FIFTH AVE N. Y. NY 10003 1981 388.
Berens, P., and others (2009). CircStat: a MATLAB toolbox for circular statistics. J Stat Softw 31, 1-21.
Bialek, W., Rieke, F., Steveninck, R. de R. van, and Warland, D. (1991). Reading a neural code. Science 252, 1854-1857.
Brasil-Neto, J.P., Valls-Solè, J., Pascual-Leone, A., Cammarota, A., Amassian, V.E., Cracco, R., Maccabee, P., Cracco, J., Hallett, M., and Cohen, L.G. (1993). Rapid modulation of human cortical motor outputs following ischaemic nerve block. Brain 116, 511-525.
Carmena, J.M., Lebedev, M.A., Crist, R.E., O'Doherty, J.E., Santucci, D.M., Dimitrov, D.F., Patil, P.G., Henriquez, C.S., and Nicolelis, M.A.L. (2003). Learning to Control a Brain-Machine Interface for Reaching and Grasping by Primates. PLOS Biol 1, e42.
Estevez, I., and Christman, M.C. (2006). Analysis of the movement and use of space of animals in confinement: the effect of sampling effort. Appl. Anim. Behav. Sci. 97, 221-240.
Fetz, E.E. (1969). Operant Conditioning of Cortical Unit Activity. Science 163, 955-958.
Fetz, E.E., and Finocchio, D.V. (1971). Operant conditioning of specific patterns of neural and muscular activity. Science 174, 431-435.
Fisher, N.I. (1995). Statistical analysis of circular data (Cambridge University Press).
Fisher, N.I., and Lee, A.J. (1992). Regression models for an angular response. Biometrics 665- 677.
Ganguly, K., and Carmena, J.M. (2009). Emergence of a Stable Cortical Map for Neuroprosthetic Control. PLOS Biol 7, e1000153. Georgopoulos, A.P., Schwartz, A.B., and Kettner, R.E. (1986). Neuronal population coding of movement direction. Science 233, 1416-1419.
Georgopoulos, A.P., Kettner, R.E., and Schwartz, A.B. (1988). Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J. Neurosci. 8, 2928-2937.
Golub, M., Chase, S., and Yu, B. (2013). Learning an Internal Dynamics Model from Control Demonstration. In ICML (1), pp. 606-614.
Green, A.M., and Kalaska, J.F. (2011). Learning to move machines with the mind. Trends Neurosci. 34, 61-75.
Humphrey, D.R., Schmidt, E.M., and Thompson, W.D. (1970). Predicting measures of motor performance from multiple cortical spike trains. Science 170, 758-762.
Jarosiewicz, B., Chase, S.M., Fraser, G.W., Velliste, M., Kass, R.E., and Schwartz, A.B. (2008). Functional network reorganization during learning in a brain-computer interface paradigm. Proc. Natl. Acad. Sci. 105, 19486-19491.
Kaelin-Lang, A., Luft, A.R., Sawaki, L., Burstein, A.H., Sohn, Y.H., and Cohen, L.G. (2002). Modulation of human corticomotor excitability by somatosensory input. J. Physiol. 540, 623-633.
Kakei, S., Hoffman, D.S., and Strick, P.L. (1999). Muscle and movement representations in the primary motor cortex. Science 285, 2136-2139.
Kim, S.-P., Simeral, J.D., Hochberg, L.R., Donoghue, J.P., and Black, M.J. (2008). Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J. Neural Eng. 5, 455.
Lebedev, M.A., Carmena, J.M., O'Doherty, J.E., Zacksenhouse, M., Henriquez, C.S., Principe, J.C., and Nicolelis, M.A. (2005). Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface. J. Neurosci. 25, 4681- 4693.
Miller, C., Christman, M.C., and Estevez, I. (2011). Movement in a confined space: Estimating path tortuosity. Appl. Anim. Behav. Sci. 135, 13-23.
Mulliken, G.H., Musallam, S., and Andersen, R.A. (2008). Decoding Trajectories from Posterior Parietal Cortex Ensembles. J. Neurosci. 28, 12913-12926.
O'Doherty, J.E., Lebedev, M.A., Ifft, P.J., Zhuang, K.Z., Shokur, S., Bleuler, H., and Nicolelis, M.A. (2011). Active tactile exploration using a brain-machine-brain interface.
Pouget, A., Deneve, S., Ducom, J.-C., and Latham, P.E. (1999). Narrow versus wide tuning curves: What's best for a population code? Neural Comput. 11, 85-90. Rao, J.S. (1969). Some contributions to the analysis of circular data.
Rao, J.S. (1972). Some variants of chi-square for testing uniformity on the circle. Probab. Theory Relat. Fields 22, 33-44.
Rao, J.S. (1976). Some tests based on arc-lengths for the circle. Sankhyā Indian J. Stat. Ser. B 329-338.
Schmidt, E.M., Bak, M.J., McIntosh, J.S., and Thomas, J.S. (1977). Operant conditioning of firing patterns in monkey cortical neurons. Exp. Neurol. 54, 467-477.
Schmidt, E.M., McIntosh, J.S., Durelli, L., and Bak, M.J. (1978). Fine control of operantly conditioned firing patterns of cortical neurons. Exp. Neurol. 61, 349-369.
Scott, S.H., Gribble, P.L., Graham, K.M., and Cabel, D.W. (2001). Dissociation between hand motion and population vectors from neural activity in motor cortex. Nature 413, 161-165.
Serruya, M., Shaikhouni, A., and Donoghue, J.P. (2003). Neural decoding of cursor motion using a Kalman filter. In Advances in Neural Information Processing Systems 15: Proceedings of the 2002 Conference, (MIT Press), p. 133.
Swindale, N.V. (1998). Orientation tuning curves: empirical description and estimation of parameters. Biol. Cybern. 78, 45-56.
Taubman, H., Vaadia, E., Paz, R., and Chechik, G. (2013). A Bayesian approach for characterizing direction tuning curves in the supplementary motor area of behaving monkeys. J. Neurophysiol. 109, 2842-2851.
Taylor, D.M., Tillery, S.I.H., and Schwartz, A.B. (2002). Direct Cortical Control of 3D Neuroprosthetic Devices. Science 296, 1829-1832.
Tehovnik, E.J., Woods, L.C., and Slocum, W.M. (2013). Transfer of information by BMI. Neuroscience 255, 134-146.
Truccolo, W., Friehs, G.M., Donoghue, J.P., and Hochberg, L.R. (2008). Primary Motor Cortex Tuning to Intended Movement Kinematics in Humans with Tetraplegia. J. Neurosci. 28, 1163-1178.
Wu, W., Gao, Y., Bienenstock, E., Donoghue, J.P., and Black, M.J. (2006). Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Comput. 18, 80-118.
Zacksenhouse, M., Lebedev, M.A., Carmena, J.M., O'Doherty, J.E., Henriquez, C., and Nicolelis, M.A.L. (2007). Cortical Modulations Increase in Early Sessions with Brain-Machine Interface. PLOS ONE 2, e619.
Zhang, K., and Sejnowski, T.J. (1999). Neuronal tuning: To sharpen or broaden? Neural Comput. 11, 75-84. |
| Date Deposited: |
18 Jan 2017 15:01 |
| Last Modified: |
19 Jan 2017 06:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/29723 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
|
![[feed]](/images/feed-icon-32x32.png){kind=icon}