在神經系統中,動作電位(neural action potentials, or spikes)在神經傳
導過程中扮演著關鍵的角色,是神經元之間用於溝通的電訊號。研究這些
訊號的處理方式對於理解大腦訊息處理機制至關重要,同時對於神經疾病
的研究也具有重要的價值。隨著科技與醫療技術的發展,讓我們可以透過
植入大腦記錄皮層內活動的多通道微處理系統來更了解神經系統的運作,
進行治療神經疾病、神經修復以及腦機接口等應用。
因此本研究的目的在於實現用於神經動作電位的檢測與壓縮的硬體系
統,將植入大腦內電極所接收到的神經訊號進行壓縮之後,傳輸到電腦上
再進行重建與人工分析,這將有助於減少神經記錄數據的儲存空間需求,
達到降低功耗以及傳輸數據量的功能,同時保留重要的神經信息並且可以
實時處理多通道的神經記錄數據,將較耗資源的運算保留到晶片外再做處
理。最後,我們對所提出的硬體系統進行了評估和驗證,實驗結果表明,
該系統能夠準確地檢測神經動作電位並實現有效的壓縮,同時保持了神經
訊號的高還原度。
綜上所述,本論文實現了一個神經動作電位檢測與壓縮演算法的硬體
系統,該系統具有高效的性能和低功耗特性。這將有助於在神經科學研究
和神經工程應用中實現高效的神經記錄和數據處理。

In the neural system, neural action potentials, also known as spikes, play a
crucial role in the process of neural communication, serving as electrical signals
between neurons. Studying the processing of these signals is essential for
understanding the brain's information processing mechanisms and holds
significant value for researching neurological disorders. With advancements in
technology and medical techniques, we can now gain a better understanding of
the functioning of the nervous system through the use of multi-channel
microelectrode systems implanted in the brain. These systems facilitate the study
and treatment of neurological diseases, neural repair, and brain-machine
interfaces, among other applications.
Therefore, the purpose of this study is to implement a hardware system for
the detection and compression of neural action potentials. The system
compresses the neural signals received by implanted brain electrodes, transmits
the compressed data to a computer for reconstruction and manual analysis. This
approach helps reduce the storage space requirements for neural recording data,
achieving lower power consumption and data transmission. At the same time, it

retains essential neural information and allows real-time processing of multi-
channel neural recording data, preserving computationally intensive tasks for off-
chip processing. Finally, we evaluated and verified the proposed hardware

system, and the experimental results demonstrated its accurate detection of neural
action potentials and effective compression while maintaining high fidelity of the
neural signals.
In conclusion, this thesis presents the implementation of a hardware system
for detecting and compressing neural action potentials, showcasing efficient

III

performance and low power consumption characteristics. This advancement will
contribute to achieving efficient neural recording and data processing in the
fields of neuro-science research and neural engineering applications.

摘要 .........................................................................................................................I
Abstract.................................................................................................................. II
致謝 ......................................................................................................................IV
章節目錄 ...............................................................................................................V
圖目錄 ................................................................................................................ VII
表格目錄 ..............................................................................................................IX
第一章 前言 ........................................................................................................1
1.1 研究動機與目的..........................................................................................1
1.2 章節架構......................................................................................................2
第二章 相關文獻回顧........................................................................................3
2.1 神經接口技術..............................................................................................3
2.1.1 腦機介面系統 .......................................................................................3
2.1.2 腦深層電刺激術 ...................................................................................4
2.2 細胞外微電極神經紀錄..............................................................................6
2.2.1 體內微電極陣列晶片(MEA)................................................................6
2.2.2 神經動作電位與局部場電位 ...............................................................7
2.2.3 單神經元活動和多神經元活動 ...........................................................8
2.3 神經動作電位訊號處理與壓縮方法..........................................................8
2.3.1 傳統神經動作電位壓縮架構 ...............................................................9
2.3.2 神經動作電位預處理與檢測 .............................................................10
2.3.3 神經動作電位壓縮與重建 .................................................................15
2.4 總結............................................................................................................19
第三章 即時神經動作電位訊號處理演算法介紹..........................................20
3.1 神經訊號預處理........................................................................................20
3.2 動作電位訊號檢測....................................................................................20
3.3 動作電位訊號對齊....................................................................................22
3.4 動作電位訊號壓縮....................................................................................23

VI

3.5 動作電位訊號重建....................................................................................24
3.6 總結............................................................................................................25
第四章 神經動作電位訊號處理演算法之軟體模擬......................................26
4.1 使用模擬資料集的單通道行為模擬...........................................................26
4.1.1 模擬資料集 .........................................................................................26
4.1.2 使用 MATLAB R2020b 進行單通道行為模擬.................................27
4.2 使用神經記錄的多通道行為模擬............................................................30
4.2.1 神經記錄資料 ......................................................................................30
4.2.2 使用 MATLAB R2020b 進行 32 通道行為模擬...............................30
4.3 總結............................................................................................................31
第五章 可擴展的 32 通道神經動作電位檢測與壓縮之數位電路設計........32
5.1 系統架構....................................................................................................32
5.2 細胞外數據預處理電路與神經動作電位檢測電路................................33
5.3 神經動作電位壓縮電路............................................................................35
5.4 在 ASIC 上的硬體實現之系統模擬結果.................................................37
5.5 與先前架構效能比較討論、效能分析....................................................39
第六章 結論與未來展望..................................................................................42
6.1 結論............................................................................................................42
6.2 未來展望....................................................................................................42
參考文獻 ..............................................................................................................44

[1] Dennis J. McFarland, Jonathan R. Wolpaw, “Brain-Computer Interface
Operation of Robotic and Prosthetic Devices” New York State Department of
Health, October 2008
[2] A Bolu Ajiboye, Francis R Willett, Daniel R Young et al. “Restoration of
reaching and grasping movements through brain-controlled muscle
stimulation in a person with tetraplegia” Mar 2017
[3] Deuschl, G., Schade-Brittinger, C. et al., “A randomized trial of deep-brain
stimulation for Parkinson's disease. New England Journal of Medicine,
355(9), 896-908.
[4] P.Krack et al., “Five-Year Follow-up of Bilateral Stimulation of the
Subthalamic Nucleus in Advanced Parkinson’s Disease” N. Engl. J. Med.,
vol. 349, no. 20, pp. 1925–1934, 2003
[5] B.Rosin et al., “Closed-loop deep brain stimulation is superior in
ameliorating parkinsonism” Neuron, vol. 72, no. 2, pp. 370–384, 2011, doi:
10.1016/j.neuron.2011.08.023.
[6] E. Fernández et al., “Acute human brain responses to intracortical
microelectrode arrays: challenges and future prospects,” Jul 2014
[7] Stavros Zanos, Theodoros P. Zanos, Vasilis Z. Marmarelis, George A.
Ojemann, and Eberhard E. Fetz, Relationships between spike-free local field
potentials and spike timing in human temporal cortex, Dec 2011
[8] G.Buzsáki, C. A.Anastassiou, andC.Koch, “The origin of extracellular fields
and currents-EEG, ECoG, LFP and spikes,” Nat. Rev. Neurosci., vol. 13, no.
6, pp. 407–420, 2012
[9] Robert A. Gaunt, Tim M. Bruns et al., “Single- and Multi-Unit Activity
Recorded from the Surface of the Dorsal Root Ganglia with Non-Penetrating
Electrode Arrays” Sep 2012
[10] Zainul Charbiwala, Vaibhav Karkare, Sarah Gibson, Dejan Markovic, Mani

45

B Srivastava, “Compressive Sensing of Neural Action Potentials Using a
Learned Union of Supports,” 2011 International Conference on Body Sensor
Networks
[11] M.Pachitariu, N.Steinmetz, S.Kadir, M.Carandini, andK.Harris, “Fast and
accurate spike sorting of high-channel count probes with KiloSort,” Adv.
Neural Inf. Process. Syst., no. Nips, pp. 4455–4463, 2016.
[12] G. C. Ray, “An algorithm to separate nonstationary part of a signal
using mid-prediction filter,” IEEE Trans. Signal Processing, vol. 42, pp.
2276–2279, Sept. 1994.

[13] S. M. Kay, Modern Spectral Estimation. Englewood Cliffs, NJ:Prentice-
Hall, 1988.

[14] Mukhopadhyay, S.; Ray, G.C. A new interpretation of nonlinear energy
operator and its efficacy in spike detection. IEEE Trans. Biomed. Eng. 1998,
45, 180–187.
[15] lames F.Kaiser, “On a simple algorithm to calculate the ‘energy’ of a
signal,” IEEE, 1990.
[16] H.Semmaoui, J.Drolet, A.Lakhssassi, andM.Sawan, “Setting adaptive spike
detection threshold for smoothed TEO based on robust statistics theory,”
IEEE Trans. Biomed. Eng., vol. 59, no. 2, pp. 474–482, 2012
[17] 馬婕芸, 32 通道即時且可擴增式神經動作電位分類演算法之硬體實現,
碩士論文, 國立清華大學, 2022.
[18] Ian H Stevenson and Konrad P Kording. How advances in neural recording
affect data analysis. Nature neuroscience, 14(2):139–142, 2011.
[19] H. Hosseini-Nejad, et al, “Data compression in brain-machine/computer
interfaces based on the Walsh-Hadamard Transform,” IEEE Transactions on
Biomedical Circuits and Systems, Vol.8, No.1, pp. 129-137, Jan. 2014.
[20] H. Kitajima, “Energy packing efficiency of the Hadamard transform,” IEEE
Trans. Commun., vol. 24, no. 11, pp. 1256–1258, 1976.

46

[21] M.A. Shaeri, & A.M. Sodagar, “A method for compression of

intracortically-recorded neural signals dedicated to implantable brain-
machine interface,” IEEE Transaction on Neural Systems and Rehabilation

Engineering, Vol. 23, No. 3, pp. 485-497, May 2015.
[22] Mahdi Nekoui; Amir M. Sodagar, et al, “Spike Compression through
Selective Downsampling and Piecewise Curve Fitting Dedicated to Neural
Recording Brain Implants,” IEEE Biomedical Circuits and Systems
Conference (BioCAS), October 2022
[23] S. Farsiani & A. M. Sodagar, "Hardware and Power-Efficient Compression
Technique Based on Discrete Tchebichef Transform for Neural Recording
Microsystems," Proc. of the 2020 IEEE Engineering in Medicine and
Biology Conference, pp. 3489-3492, 2020.
[24] R. Q.Quiroga, “Simulated data,” University of Leicester. Dataset., 2020.
https://doi.org/10.25392/leicester.data.11897595.v1.
[25] K.Sahasrabuddhe et al., “The Argo: A high channel count recording system
for neural recording in vivo,” J. Neural Eng., vol. 18, no. 1, pp. 1–32, 2021