臺灣博碩士論文加值系統

在閉迴路腦機介面的領域中，感覺編碼模型負責作為外部環境資訊與大腦相互溝通的橋樑，並將外部所接收到的資訊編碼成大腦所能夠理解的訊息，目前感覺編碼模型有兩種建立方法，其一是使用心理學行為學的方式，其二是使用神經電生理學的方式；心理學行為學的方式將透過不同設定參數進行顱內電刺激，並透過行為試驗方法建立心理性等價關係函式，觀察與真實感覺刺激有相同辨識度的電刺激參數，然而，心理學行為學的方式難以在動物模型上探討不同電刺激量的實際感覺，以及在動作控制中進行感覺量化的探討；近幾年來，研究人員透過神經電生理學的方式，發現調整顱內電刺激的振幅參數，能夠產生與真實觸覺刺激相似的局部場電位，此外，過去研究發現觸覺機械性刺激的速度會在感覺皮質區產生明顯的放電反應，且無論是在高頻的動作電位或低頻的局部場電位，都能夠作為編碼模型的神經訊號參數，因此我們希望能夠藉由神經訊號的記錄來建立類似於等價關係函式的編碼模型，然而，在感覺解碼上發現局部場電位的解碼比動作電位來的穩定，因此，在即時的雙向腦機介面的精準度與穩定性上，局部場電位較適合作為我們的感覺編碼模型。
本篇研究會進行兩個在清醒動物行為下的壓桿實驗，來分別記錄不同壓桿速度以及顱內電刺激量的前肢感覺皮質區神經放電反應，並藉由神經訊號時域與時頻訊號的特徵擷取，找到與刺激相關的參數以產生線性或非線性回歸模型，來建構局部場電位的感覺編碼模型，並討論透過神經反應等價關係建立的編碼模型之結果，以及動作電位的編碼模型之差異。

The encoder in closed-loop brain-machine interfaces (BMIs) plays an important role in establishing a direct communication link between the brain and the external world. There are two methods to build up an encoder, the psychometric equivalence approach and neurophysiological approach. The psychometric equivalence function (PEF) is established by assessing the same performance of detection toward both different parameter of intracortical microstimulation (ICMS) and the mechanical stimulation. However, it’s hard to observe the quality and the quantity of the sensation evoked by ICMS. In the recent research, scientists found out that ICMS could elicit the naturalistic cortical response. Besides, somatosensory cortex, whether in neural firing rate or local field potentials (LFPs), is sensitive to the different velocity of tactile stimulus. As the result, in our research, we propose a stimulus evoked potential (SEP)-based encoder of sensory cortical system which was built up by the concept of PEF. In the past research, compare with firing rate, the LFPs based decoding model is more robust in stimulus decoding for its comprehensive information. For establishing a stable and precise sensory SEP-based encoder of sensory cortical system for the real-time closed-loop BMI model, LFPs would be more suitable.
In our study, we’re going to build up a SEP-based encoder in behavioral rat by recording the evoked potential from acceleration stimulus of lever-pressing and the ICMS. By extracting the features from LFP, we could find the stimulus-correlated features for the SEP-based encoder. The SEP-based encoder be established by the linear regression models, logistic regression model, and exponential regression model. Furthermore, we would discuss the result of our SEP-based encoder, and compare the stability and precision between spike-based and SEP-based encoder.

Acknowledgments i
摘要 ii
Abstract iii
Table of Contents iv
List of Figures vi
List of Tables vii
Chapter 1. Introduction 1
1.1 Literature Review 2
1.2 Motivation and Objective 8
1.3 Structure of thesis neurophysiological 9
Chapter 2. Materials and Methods 10
2.1 The architecture of SEP-based encoder of sensory cortical system 10
2.2 Animal preparation 12
2.3 In vivo data collection and preprocessing.......14
2.4 SEP waveform characterizing to encode sensory information 16
2.4.1 Feature extraction 16
2.4.2 Lever-pressing encoding function and ICMS transform function 19
2.5 Evaluation of SEP-based Encoder of Sensory Cortical System 20
Chapter 3. Experimental Results 22
3.1 In vivo data collection and preprocessing 22
3.2 The correlation between different characteristic of SEP waveform and the velocity of lever-pressing 25
3.3 Lever-pressing encoding function and ICMS transform function 27
3.4 Approach to SEP-based Encoder of Sensory Cortical System 27
3.5 Evaluation of SEP-based Encoder of Sensory Cortical System 28
Chapter 4. Discussion 31
4.1 Influence of Feature Extraction 31
4.2 Identification of lever-pressing encoding function and ICMS transform function 31
4.3 Performance of SEP-based encoder of sensory cortical system 32
Chapter 5. Supplementary Materials 33
Chapter 6. Reference 48


List of Figures
Figure 1. Schematic of a closed-loop brain-machine interface. 3
Figure 2. The architecture of the SEP-based encoder of sensory cortical system 11
Figure 3. The paradigm of water-reward lever-pressing behavioral training. 14
Figure 4. The process of establish the SEP-based encoder of sensory cortical system 15
Figure 5. The illustration of SEP waveform characterizing 19
Figure 6. The configuration of the floor plan in the behavioral box. 21
Figure 7. The schematic of the neural signals of lever-pressing framework and S1FL cortex ICMS framework. 23
Figure 8. The schematic of the segmented SEP for feature extraction. 24
Figure 9. The latency of peak and valley in two types of stimulation. 25
Figure 10. The configuration of “Area_mean_BL” and “Square_mean_BL” 26
Figure 11. The amplitude of ICMS for encoding the velocity of lever-pressing from the SEP-based encoder of three subjects. 28
Figure 12. The illustration of natural neural response that evoked by lever-pressing and the virtual neural response that evoked by ICMS 29
Figure 13. The image of behavioral assessment. (Successful trial = 63.1 ± 10.4 %) 30
Figure 14. The illustration of the firing rate of spontaneous and respond channels in each subject. 34
Figure 15. The illustration of the platform of ICMS control system. 37
Figure 16. The illustration of the ICMS area on the screen. As the CCD captured the rat’s forelimb in the ICMS area, the red doted square, the isolated stimulator would be trigger in the same time. 38


List of Tables
Table 1. The development of the encoder in brain machine interface. 7
Table 2. The illustration of features and the corresponding equations where

[1] J. J. Gibson, “Observations on active touch,” Psychological review, vol. 69, no. 6, pp. 477, 1962.
[2] K. R. Boff, L. Kaufman, and J. P. Thomas, “Workload assessment methodology,” Handbook of perception and human performance, vol. 2, pp. 42-26, 1986.
[3] S. J. Lederman, and R. L. Klatzky, “Hand movements: A window into haptic object recognition,” Cognitive psychology, vol. 19, no. 3, pp. 342-368, 1987.
[4] J. M. Loomis, and S. J. Lederman, “Tactual perception,” Handbook of perception and human performance, vol. 2, pp. 31-2, 1986.
[5] A. Saig, G. Gordon, E. Assa, A. Arieli, and E. Ahissar, “Motor-sensory confluence in tactile perception,” Journal of Neuroscience, vol. 32, no. 40, pp. 14022-14032, 2012.
[6] M. Kawato, “Internal models for motor control and trajectory planning,” Current opinion in neurobiology, vol. 9, no. 6, pp. 718-727, 1999.
[7] G. Gordon, and E. Ahissar, “Hierarchical curiosity loops and active sensing,” Neural Networks, vol. 32, pp. 119-129, 2012.
[8] M. Barbiero, C. Rousseau, C. Papaxanthis, and O. White, “Coherent multimodal sensory information allows switching between gravitoinertial contexts,” Frontiers in physiology, vol. 8, pp. 290, 2017.
[9] N. A. Bernstein, Human motor actions: Bernstein reassessed: North-Holland, 1984.
[10] R. Romo, A. Hernández, A. Zainos, and E. Salinas, “Somatosensory discrimination based on cortical microstimulation,” Nature, vol. 392, no. 6674, pp. 387-390, 1998.
[11] R. Romo, A. Hernández, A. Zainos, C. D. Brody, and L. Lemus, “Sensing without touching: psychophysical performance based on cortical microstimulation,” Neuron, vol. 26, no. 1, pp. 273-278, 2000.
[12] S. K. Talwar, S. Xu, E. S. Hawley, S. A. Weiss, K. A. Moxon, and J. K. Chapin, “Behavioural neuroscience: Rat navigation guided by remote control,” Nature, vol. 417, no. 6884, pp. 37-38, 2002.
[13] J. E. O'Doherty, M. A. Lebedev, T. L. Hanson, N. A. Fitzsimmons, and M. A. Nicolelis, “A brain-machine interface instructed by direct intracortical microstimulation,” Frontiers in integrative neuroscience, vol. 3, pp. 20, 2009.
[14] S. N. Flesher, J. L. Collinger, S. T. Foldes, J. M. Weiss, J. E. Downey, E. C. Tyler-Kabara, S. J. Bensmaia, A. B. Schwartz, M. L. Boninger, and R. A. Gaunt, “Intracortical microstimulation of human somatosensory cortex,” Science translational medicine, vol. 8, no. 361, pp. 361ra141-361ra141, 2016.
[15] M. Semprini, L. Bennicelli, and A. Vato, “A parametric study of intracortical microstimulation in behaving rats for the development of artificial sensory channels,” 2012 Annual International Conference of the IEEE, pp. 799-802, 2012.
[16] J. Berg, J. Dammann III, F. Tenore, G. Tabot, J. Boback, L. Manfredi, M. Peterson, K. Katyal, M. Johannes, and A. Makhlin, “Behavioral demonstration of a somatosensory neuroprosthesis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 3, pp. 500-507, 2013.
[17] S. Kim, T. Callier, G. A. Tabot, R. A. Gaunt, F. V. Tenore, and S. J. Bensmaia, “Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex,” Proceedings of the National Academy of Sciences, vol. 112, no. 49, pp. 15202-15207, 2015.
[18] G. A. Tabot, S. S. Kim, J. E. Winberry, and S. J. Bensmaia, “Restoring tactile and proprioceptive sensation through a brain interface,” Neurobiology of disease, vol. 83, pp. 191-198, 2015.
[19] C. D. Salzman, K. H. Britten, and W. T. Newsome, “Cortical microstimulation influences perceptual judgements of motion direction,” Nature, vol. 346, no. 6280, pp. 174-177, 1990.
[20] R. Romo, A. Hernandez, A. Zainos, and E. Salinas, “Somatosensory discrimination based on cortical microstimulation,” Nature, vol. 392, no. 6674, pp. 387, 1998.
[21] S. Butovas, and C. Schwarz, “Detection psychophysics of intracortical microstimulation in rat primary somatosensory cortex,” European Journal of Neuroscience, vol. 25, no. 7, pp. 2161-2169, 2007.
[22] G. A. Tabot, J. F. Dammann, J. A. Berg, F. V. Tenore, J. L. Boback, R. J. Vogelstein, and S. J. Bensmaia, “Restoring the sense of touch with a prosthetic hand through a brain interface,” Proceedings of the National Academy of Sciences, vol. 110, no. 45, pp. 18279-18284, 2013.
[23] S. Kim, T. Callier, G. A. Tabot, F. V. Tenore, and S. J. Bensmaia, “Sensitivity to microstimulation of somatosensory cortex distributed over multiple electrodes,” Frontiers in systems neuroscience, vol. 9, pp. 47, 2015.
[24] J. S. Choi, A. J. Brockmeier, D. B. McNiel, L. M. Von Kraus, J. C. Príncipe, and J. T. Francis, “Eliciting naturalistic cortical responses with a sensory prosthesis via optimized microstimulation,” Journal of neural engineering, vol. 13, no. 5, pp. 056007, 2016.
[25] C. R. Holdgraf, J. W. Rieger, C. Micheli, S. Martin, R. T. Knight, and F. E. Theunissen, “Encoding and decoding models in cognitive electrophysiology,” Frontiers in systems neuroscience, vol. 11, pp. 61, 2017.
[26] G. Buzsáki, C. A. Anastassiou, and C. Koch, “The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes,” Nature reviews neuroscience, vol. 13, no. 6, pp. 407-420, 2012.
[27] L. Li, J. S. Choi, J. T. Francis, J. C. Sanchez, and J. C. Principe, “Decoding stimuli from multi-source neural responses,” 2012 Annual International Conference of the IEEE, pp. 1331-1334, 2012.
[28] A. Belitski, S. Panzeri, C. Magri, N. K. Logothetis, and C. Kayser, “Sensory information in local field potentials and spikes from visual and auditory cortices: time scales and frequency bands,” Journal of computational neuroscience, vol. 29, no. 3, pp. 533-545, 2010.
[29] J. R. Huxter, T. J. Senior, K. Allen, and J. Csicsvari, “Theta phase–specific codes for two-dimensional position, trajectory and heading in the hippocampus,” Nature neuroscience, vol. 11, no. 5, pp. 587-594, 2008.
[30] E. D. Adrian, The basis of sensation: Christophers; London, 22 Berners Steeet, W. 1, 1928.
[31] L. Paninski, E. P. Simoncelli, and J. W. Pillow, “Maximum likelihood estimation of a stochastic integrate-and-fire neural model, ” Advances in Neural Information Processing Systems, pp. 1311-1318, 2004.
[32] S. S. Kim, A. P. Sripati, and S. J. Bensmaia, “Predicting the timing of spikes evoked by tactile stimulation of the hand,” Journal of neurophysiology, vol. 104, no. 3, pp. 1484-1496, 2010.
[33] A.-R. Boloori, R. A. Jenks, G. Desbordes, and G. B. Stanley, “Encoding and decoding cortical representations of tactile features in the vibrissa system,” Journal of Neuroscience, vol. 30, no. 30, pp. 9990-10005, 2010.
[34] P. H. Thakur, P. J. Fitzgerald, and S. S. Hsiao, “Second-order receptive fields reveal multidigit interactions in area 3b of the macaque monkey,” Journal of Neurophysiology, vol. 108, no. 1, pp. 243-262, 2012.
[35] D. R. Humphrey, and E. M. Schmidt, “Extracellular single-unit recording methods,” Neurophysiological techniques: Applications to neural systems, pp. 1-64, 1990.
[36] S.-P. Kim, F. Wood, M. Fellows, J. P. Donoghue, and M. J. Black, “Statistical analysis of the non-stationarity of neural population codes, ” The First IEEE/RAS-EMBS International Conference on, pp. 811-816, 2006.
[37] D. Szarowski, M. Andersen, S. Retterer, A. Spence, M. Isaacson, H. Craighead, J. Turner, and W. Shain, “Brain responses to micro-machined silicon devices,” Brain research, vol. 983, no. 1, pp. 23-35, 2003.
[38] A. Mazzoni, S. Panzeri, N. K. Logothetis, and N. Brunel, “Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons,” PLoS computational biology, vol. 4, no. 12, pp. e1000239, 2008.
[39] U. Mitzdorf, “Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena,” Physiological reviews, vol. 65, no.1, pp. 37-100, 1985.
[40] O. Herreras, “Local Field Potentials: Myths and Misunderstandings,” Frontiers in neural circuits, vol. 10, pp. 101, 2016.
[41] K. Johnson, “Reconstruction of population response to a vibratory stimulus in quickly adapting mechanoreceptive afferent fiber population innervating glabrous skin of the monkey,” Journal of Neurophysiology, vol. 37, no. 1, pp. 48-72, 1974.
[42] S. J. Bensmaia, “Tactile intensity and population codes,” Behavioural brain research, vol. 190, no. 2, pp. 165-173, 2008.
[43] L. Li, A. J. Brockmeier, J. S. Choi, J. T. Francis, J. C. Sanchez, and J. C. Príncipe, “A tensor-product-kernel framework for multiscale neural activity decoding and control,” Computational intelligence and neuroscience, vol. 2014, pp. 2, 2014.
[44] A. J. Brockmeier, J. S. Choi, E. G. Kriminger, J. T. Francis, and J. C. Principe, “Neural decoding with kernel-based metric learning,” Neural computation, vol. 26, no. 6, pp. 1080-1107, 2014.
[45] J. Morissette, and J. M. Bower, “Contribution of somatosensory cortex to responses in the rat cerebellar granule cell layer following peripheral tactile stimulation,” Experimental brain research, vol. 109, no. 2, pp. 240-250, 1996.
[46] L. Roggeri, B. Rivieccio, P. Rossi, and E. D'Angelo, “Tactile stimulation evokes long-term synaptic plasticity in the granular layer of cerebellum,” Journal of Neuroscience, vol. 28, no. 25, pp. 6354-6359, 2008.
[47] H. Parasuram, B. Nair, G. Naldi, E. D’Angelo, and S. Diwakar, “Understanding Cerebellum Granular Layer Network Computations through Mathematical Reconstructions of Evoked Local Field Potentials,” Annals of Neurosciences, vol. 25, no. 1, pp. 11-24, 2018.
[48] I. Hashimoto, T. Gatayama, K. Yoshikawa, and M. Sasaki, “Somatosensory evoked potential correlates of psychophysical magnitude estimations for air-puff stimulation of the face in man,” Experimental brain research, vol. 88, no. 3, pp. 639-644, 1992.
[49] R. P. Lesser, R. Koehle, and H. Lueders, “Effect of stimulus intensity on short latency somatosensory evoked potentials,” Electroencephalography and clinical neurophysiology, vol. 47, no. 3, pp. 377-382, 1979.
[50] Y. Nakajima, and N. Imamura, “Relationships between attention effects and intensity effects on the cognitive N140 and P300 components of somatosensory ERPs,” Clinical Neurophysiology, vol. 111, no. 10, pp. 1711-1718, 2000.
[51] Y. Zhang, and M. Ding, “Detection of a weak somatosensory stimulus: Role of the prestimulus mu rhythm and its top–down modulation,” Journal of cognitive neuroscience, vol. 22, no. 2, pp. 307-322, 2010.
[52] J. W. Kuziek, and K. E. Mathewson, “Does viewing nature and urban environments change neuro-cognitive markers of attention?,” PsyArXiv, 2017.
[53] J. Friedman, and R. Meares, “Cortical evoked potentials and extraversion,” Psychosomatic Medicine, vol. 41, no. 4, pp. 279-286, 1979.
[54] V. Scaioli, M. Brinciotti, M. Di Capua, S. Lori, A. Janes, G. Pastorino, C. Peruzzi, P. Sergi, and A. Suppiej, “A multicentre database for normative brainstem auditory evoked potentials (BAEPs) in children: methodology for data collection and evaluation,” The open neurology journal, vol. 3, pp. 72, 2009.
[55] F. Zhang, J. Eliassen, J. Anderson, P. Scheifele, and D. Brown, “The time course of the amplitude and latency in the auditory late response evoked by repeated tone bursts,” Journal of the American Academy of Audiology, vol. 20, no. 4, pp. 239-250, 2009.
[56] F. Zhang, C. Benson, D. Murphy, M. Boian, M. Scott, R. Keith, J. Xiang, and P. Abbas, “Neural adaptation and behavioral measures of temporal processing and speech perception in cochlear implant recipients,” PloS one, vol. 8, no. 12, pp. e84631, 2013.
[57] J. A. Gottfried, Neurobiology of sensation and reward: CRC Press, 2011.
[58] M. G. Mladejovsky, D. K. Eddington, J. R. Evans, and W. H. Dobelle, “A computer-based brain stimulation system to investigate sensory prostheses for the blind and deaf,” IEEE Transactions on Biomedical Engineering, no. 4, pp. 286-296, 1976.
[59] R. D. Flint, E. W. Lindberg, L. R. Jordan, L. E. Miller, and M. W. Slutzky, “Accurate decoding of reaching movements from field potentials in the absence of spikes,” Journal of neural engineering, vol. 9, no. 4, pp. 046006, 2012.
[60] A. Bar-Hillel, A. Spiro, and E. Stark, “Spike sorting: Bayesian clustering of non-stationary data, ” In Advances in Neural Information Processing Systems, pp. 105-112, 2005.
[61] H. G. Rey, C. Pedreira, and R. Q. Quiroga, “Past, present and future of spike sorting techniques,” Brain research bulletin, vol. 119, pp. 106-117, 2015.
[62] A. D. Legatt, J. Arezzo, and H. G. Vaughan, “Averaged multiple unit activity as an estimate of phasic changes in local neuronal activity: effects of volume-conducted potentials,” Journal of neuroscience methods, vol. 2, no. 2, pp. 203-217, 1980.
[63] J. Kimura, T. Yamada, and D. D. Walker, “Theory of Near-Field and Far-Field Evoked Potentials, ” Advanced Evoked Potentials, pp. 1-28: Springer, 1989.
[64] J. Walker, D. Halliday, and R. Resnick, Fundamentals of physics: Hoboken, NJ: Wiley, 2008.
[65] I. MathWorks, Curve fitting toolbox: for use with MATLAB®: user's guide: MathWorks, 2002.
[66] J. F. Hair, C. M. Ringle, and M. Sarstedt, “PLS-SEM: Indeed a silver bullet,” Journal of Marketing theory and Practice, vol. 19, no. 2, pp. 139-152, 2011.
[67] J. F. Hair, C. M. Ringle, and M. Sarstedt, “Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance,” Long Range Planning, vol. 46, issues 1-2, pp. 1-12, 2013.
[68] B. Tutunculer, G. Foffani, B. T. Himes, and K. A. Moxon, “Structure of the Excitatory Receptive Fields of Infragranular Forelimb Neurons in the Rat Primary Somatosensory Cortex Responding To Touch,” Cerebral Cortex, vol. 16, no. 6, pp. 791-810, 2006.