( 您好!臺灣時間:2023/06/03 22:44
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::


論文名稱(外文):Effective Dimension Reduction by Sliced Inverse Regression for Neural Decoding in Primary Motor Cortex
外文關鍵詞:Sliced Inverse RegressionBrain Machine InterfaceDecoding AlgorithmPrimary Motor Cortex
  • 被引用被引用:0
  • 點閱點閱:187
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:2
在本研究裡,我們設計了一個老鼠實驗去驗證我們所開發的演算法,薄片逆回歸。薄片逆回歸利用腦部的神經訊號以及從影像中所擷取的軌跡來建立模型,並且跟目前已經存在這領域的兩個演算法,群體向量法(Population Vector Algorithm) 和最佳線性估測法(Optimal Linear Estimation)做比較。而在軌跡預測結果的方均根誤差中,我們發現薄片逆回歸的方法比群體向量法低了28.26%、比最佳現性估計法低了35.19%,並且在軌跡預測的相關係數中達到(0.8729,0.8647) 的高相關度,因此證明了我們所運用的新方法比另外兩個線性的方法還要好。除了演算法開發之外,我們也發現老鼠在做壓水動作的期間,場電位在12赫茲以下出現了能量上升的情形,間接證明了我們所收到的神經訊號是與此壓桿行為是有關係的,因此得知我們用此訊號來開發演算法是合理的。
In the neuroscience field, one of the key researches is Brain Machine Interfaces (BMIs). Neural decoding algorithm development plays an important role in BMIs’ investigates. Decoding algorithm in primary motor cortex is able to achieve the goal about controlling the external computer cursors or prosthetic limbs. Many algorithms have been developed in current, and population vector algorithm has been implemented in real time among of linear decoding. The data dimension in this field has become more and more plenty with the implanted electrode arrays increasing. Therefore, we want to develop a decoding algorithm is capable of extracting intrinsic information and reducing the data dimension.
In our research, we have designed a depressed experiment for rat to demonstrate our developed algorithm, Sliced Inverse Regression (SIR), which is used to build a model between neural activities from brain and forelimb’s trajectory from video. Moreover, compare the prediction with two current linear decoding algorithms, Population Vector Algorithm (PVA) and Optimal Linear Estimation (OLE). In the root-mean square error criterion (RMSE), SIR has lower RMSE in 28.26% than PVA, and lower in 35.19% than OLE. Additionally, SIR achieved a high correlation (0.8729, 0.8647) at trajectory prediction. Therefore, we can know that SIR has a better prediction than other in our case. In addition to algorithm development, the specific pattern which the power had an obvious increasing under the 7~12 Hz for the power spectrogram of local field potential was occurred during behavior task. Therefore, indirectly prove the relationship between neural activities and behavior task so that the neural activities were significant to develop decoding algorithm.
摘 要 II
誌 謝 III
Table of Contents IV
List of Tables VI
List of Figures VII
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Literature Review 3
1.3 Motivations and Objectives 6
1.4 Thesis Organization 8
Chapter 2 Materials and Methods 9
2.1 Animal Preparation and Experiments Design 9
2.2 Data Recording and Preprocess 11
2.3 Statistical Analysis 14
2.3.1 Sliced Inverse Regression 15 Forward and Inverse Regression 15 An algorithm of SIR 16 Principle Component Analysis (PCA) and SIR 19 Effective Dimension Reduction in Regression 22
2.3.2 Other Linear Decoding Methods 23 Population Vector Algorithm 23 Optimal Linear Estimation 24
2.4 Estimating the Optimal Time Lag and Order 25
Chapter 3 Results 27
3.1 Neural Signal Pattern during behavior task 27
3.1.1 Local Field Potential 27
3.1.2 Neural Activity 30
3.2 Trajectory Prediction 32
3.2.1 Trajectory Plot 32
3.2.2 Statistical Data Analysis 34
3.3 SIR’s Axes selection 42
Chapter 4 Discussion 46
4.1 Significance Verification for Neural Signal 46
4.2 Comparison of Decoding Algorithm 46
4.2.1 Experimental Subject Influence 46
4.2.2 Algorithm discussion 47
4.2.3 Time Lag and Time Order Issue 48
Chapter 5 Conclusion and Future Works 50
5.1 Conclusion 50
5.2 Future Works 51
References 52
[1] M. D. Linderman, G. Santhanam, C. T. Kemere, V. Gilja, S. O'Driscoll, B. M. Yu, A. Afshar, S. I. Ryu, K. V. Shenoy, and T. H. Meng, "Signal Processing Challenges for Neural Prostheses," IEEE Signal Processing Magazine, vol. 25, pp. 18-28, 2008.
[2] L. R. Hochberg, M. D. Serruya, G. M. Friehs, J. A. Mukand, M. Saleh, A. H. Caplan, A. Branner, D. Chen, R. D. Penn, and J. P. Donoghue, "Neuronal ensemble control of prosthetic devices by a human with tetraplegia," Nature, vol. 442, pp. 164-171, 2006.
[3] J. K. Chapin, K. A. Moxon, R. S. Markowitz, and M. A. L. Nicolelis, "Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex," Nature Neuroscience, vol. 2, pp. 664-670, 1999.
[4] D. M. Taylor, S. I. H. Tillery, and A. B. Schwartz, "Direct Cortical Control of 3D Neuroprosthetic Devices," Science, vol. 296, pp. 1829-1832, 2002.
[5] M. Velliste, S. Perel, M. C. Spalding, A. S. Whitford, and A. B. Schwartz, "Cortical control of a prosthetic arm for self-feeding," Nature, vol. 453, pp. 1098-1101, 2008.
[6] J. K. C. Theodore W. Berger, Greg A. Gerhardt, Dennis J. McFarland, José C. Principe, Walid V. Soussou, Dawn M. Taylor and Patrick A. Tresco, Brain-Computer Interfaces: Springer Netherlands, 2008.
[7] M. A. Lebedev and M. A. L. Nicolelis, "Brain-machine interfaces: past, present and future," Trends in Neurosciences, vol. 29, pp. 536-546, 2006.
[8] R. R. Harrison, P. T. Watkins, R. J. Kier, R. O. Lovejoy, D. J. Black, B. Greger, and F. Solzbacher, "A low-power integrated circuit for a wireless 100-electrode neural recording system," IEEE Journal of Solid State Circuits, vol. 42, p. 123, 2007.
[9] Z. S. Zumsteg, C. Kemere, S. O'Driscoll, G. Santhanam, R. E. Ahmed, K. V. Shenoy, and T. H. Meng, "Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, pp. 272-279, 2005.
[10] A. B. Schwartz and D. W. Moran, "Motor Cortical Activity During Drawing Movements: Population Representation During Lemniscate Tracing," Journal of Neurophysiology, vol. 82, pp. 2705-2718, 1999.
[11] J. Wessberg, C. R. Stambaugh, J. D. Kralik, P. D. Beck, M. Laubach, J. K. Chapin, J. Kim, S. J. Biggs, M. A. Srinivasan, and M. A. L. Nicolelis, "Real-time prediction of hand trajectory by ensembles of cortical neurons in primates," Nature, vol. 408, pp. 361-365, 2000.
[12] M. M. Morrow and L. E. Miller, "Prediction of Muscle Activity by Populations of Sequentially Recorded Primary Motor Cortex Neurons," Journal of Neurophysiology, vol. 89, pp. 2279-2288, 2003.
[13] S. Kakei, D. S. Hoffman, and P. L. Strick, "Muscle and Movement Representations in the Primary Motor Cortex," Science, vol. 285, pp. 2136-2139, 1999.
[14] A. P. Georgopoulos, J. F. Kalaska, R. Caminiti, and J. T. Massey, "On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex," Journal of Neuroscience, vol. 2, pp. 1527-1537, 1982.
[15] R. E. Kettner, A. B. Schwartz, and A. P. Georgopoulos, "Primate motor cortex and free arm movements to visual targets in three- dimensional space. III. Positional gradients and population coding of movement direction from various movement origins," Journal of Neuroscience, vol. 8, pp. 2938-2947, 1988.
[16] E. Salinas and L. F. Abbott, "Vector reconstruction from firing rates," Journal of Computational Neuroscience, vol. 1, pp. 89-107, 1994.
[17] Howard B. Eichenbaum and J. L. Davis, "Neuronal Ensembles," 1998.
[18] E. N. Brown, L. M. Frank, D. Tang, M. C. Quirk, and M. A. Wilson, "A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells," Journal of Neuroscience, vol. 18, pp. 7411-7425, 1998.
[19] A. E. Brockwell, A. L. Rojas, and R. E. Kass, "Recursive Bayesian Decoding of Motor Cortical Signals by Particle Filtering," Journal of Neurophysiology, vol. 91, pp. 1899-1907, 2004.
[20] W. Wu, Y. Gao, E. Bienenstock, J. P. Donoghue, and M. J. Black, "Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter," Neural Computation, vol. 18, pp. 80-118, 2006.
[21] W. Wu, M. J. Black, D. Mumford, Y. Gao, E. Bienenstock, and J. P. Donoghue, "Modeling and decoding motor cortical activity using a switching Kalman filter," IEEE Transactions on Biomedical Engineering, vol. 51, pp. 933-942, 2004.
[22] B. M. Yu, C. Kemere, G. Santhanam, A. Afshar, S. I. Ryu, T. H. Meng, M. Sahani, and K. V. Shenoy, "Mixture of Trajectory Models for Neural Decoding of Goal-Directed Movements," Journal of Neurophysiology, vol. 97, pp. 3763-3780, 2007.
[23] C. S. Justin, M. Babak, D. Jack, and C. P. Jose, "2009 Special Issue: Exploiting co-adaptation for the design of symbiotic neuroprosthetic assistants," Neural Network, vol. 22, pp. 305-315, 2009.
[24] S. Lin, J. Si, and A. B. Schwartz, "Self-Organization of Firing Activities in Monkey's Motor Cortex: Trajectory Computation from Spike Signals," Neural Computation, vol. 9, pp. 607-621, 1997.
[25] M. D. Serruya, N. G. Hatsopoulos, L. Paninski, M. R. Fellows, and J. P. Donoghue, "Brain-machine interface: Instant neural control of a movement signal," Nature, vol. 416, pp. 141-142, 2002.
[26] G. Santhanam, S. I. Ryu, B. M. Yu, A. Afshar, and K. V. Shenoy, "A high-performance brain-computer interface," Nature, vol. 442, pp. 195-198, 2006.
[27] K.-C. Li, "Sliced Inverse Regression for Dimension Reduction," Journal of The American Statistical Association, vol. 86, pp. 316-327, 1991.
[28] K. C. Li, "High dimensional data analysis via the SIR/PHD approach," 2000.
[29] J. P. Donoghue, J. N. Sanes, N. G. Hatsopoulos, and G. Gaal, "Neural Discharge and Local Field Potential Oscillations in Primate Motor Cortex During Voluntary Movements," Journal of Neurophysiology, vol. 79, pp. 159-173, 1998.
[30] W. Jensen and J. Rousche, "Movement Discrimination Based On Rat Primary Motor Cortex Responses," Proceedings of The 2nd International IEEE EMBS Conference on Neural Engineering, pp. 559-562, 2005.
[31] N. S. Narayanan, E. Y. Kimchi, and M. Laubach, "Redundancy and Synergy of Neuronal Ensembles in Motor Cortex," Journal of Neuroscience, vol. 25, pp. 4207-4216, 2005.
[32] A. B. Schwartz, D. M. Taylor, and S. I. H. Tillery, "Extraction algorithms for cortical control of arm prosthetics," Current Opinion in Neurobiology, vol. 11, pp. 701-708, 2001.
[33] J. C. Sanchez, S. P. Kim, D. Erdogmus, Y. N. Rao, J. C. Principe, J. Wessberg, and M. Nicolelis, "Input-output mapping performance of linear and nonlinear models for estimating hand trajectories from cortical neuronal firing patterns," 2002, pp. 139–148.
[34] J. R. Schott, "Determining the Dimensionality in Sliced Inverse Regression," Journal of the American Statistical Association, vol. 89, pp. 141-148, 1994.
[35] J. B. Willett and J. D. Singer, "Another Cautionary Note About R square: Its Use in Weighted Least-Squares Regression Analysis," The American Statistician, vol. 42, pp. 236-238, 1988.
[36] JOKaML Recce, "Phase relationship between hippocampal place units and the EEG theta rhythm," Hippocampus, vol. 3, pp. 317-330, 1993
[37] T Britvina and. Eggermont JJ, "Multi-frequency auditory stimulation disrupts spindling activity in anesthetized animals," Neuroscience, vol. 151, pp. 888-900, 2008
第一頁 上一頁 下一頁 最後一頁 top