這項工作致力於開發一種複雜的功能性電刺激器(FES)，它結合了可編程刺激和自主補償功能，從而以抑制癲癇發作為目標。FES 利用數字合成電路，包括精心設計的串行外圍接口和狀態機，促進刺激系統內的無縫通信和控制。值得注意的是，功能性電刺激器(FES) 擁有多種可編程功能，允許選擇雙波形選項、微調電流強度水平、靈活定制刺激配置以及精確選擇通道。這種全面的功能允許用戶根據他們的獨特需求定制FES，優化其在廣泛應用中的性能和多功能性。通過提供這種可編程性，FES 使臨床醫生和研究人員能夠根據個體患者的獨特需求定制刺激參數。此外，功能性電刺激器(FES) 集成了利用推挽放大器的自主補償機制。
此功能可以監控電極電壓，並通過負反饋迴路補償任何過多的剩餘電荷。擬議的芯片採用0.18μm CMOS 工藝設計和製造。
與台灣大學獸醫學院合作，精心進行了全面的動物實驗，以評估所提出的功能性電刺激芯片的有效性和生物安全性。實驗方案經過精心設計和執行。利用由戊四唑(PTZ) 誘導的廣泛接受的小鼠模型進行了綜合評估。通過嚴格的數據分析和驗證，結果明確證實了該芯片實現了顯著的癲癇發作抑制。這一重大貢獻展示了所提出的功能性電刺激芯片在生物醫學研究和神經系統疾病治療中的巨大潛力。

This dissertation endeavors to develop a sophisticated functional electrical stimulator (FES)
that incorporates programmable stimulation and autonomous compensation capabilities,
thereby targeting the suppression of seizures. Leveraging a digitally synthesized circuit,
the FES encompasses meticulously designed serial peripheral interface and state machine,
facilitating seamless communication and control within the stimulation system.
Remarkably, the functional electrical stimulator (FES) boasts a versatile repertoire of
programmable features, allowing for the selection of dual-waveform options, fine-tuning
of current intensity levels, flexible customization of stimulus configurations, and precise
channel selection. This comprehensive range of capabilities allows users to customize
the FES according to their unique requirements, optimizing its performance and versatility
across a wide range of applications. By affording this programmability, the FES
empowers clinicians and researchers to tailor the stimulation parameters to the unique requirements
of individual patients. Moreover, the functional electrical stimulator (FES)
integrates autonomous compensation mechanisms utilizing a push-pull amplifier. This
feature enables the monitoring of electrode voltage and subsequent compensation of any
excessive residue cexcessiveugh a negative feedback loop. The proposed chip is designed
and fabricated in a 0.18μm CMOS process.

In collaboration with the School of Veterinary Medicine at National Taiwan University,
a comprehensive animal experiment was meticulously conducted to assess the
effectiveness and biosafety of the proposed functional electrical stimulation chip. The experimental
protocol was carefully designed and executed. A comprehensive evaluation was performed utilizing a widely accepted mouse model induced by Pentylenetetrazol (PTZ). Through rigorous data analysis and verification, the results unequivocally confirmed the remarkable seizure suppression achieved by the chip. This significant contribution
showcases the promising potential of the proposed functional electrical stimulation
chip in biomedical research and the treatment of neurological disorders.

[1] F. Rattay, Electrical nerve stimulation. Springer, 1990.
[2] H.-M. Lee, K. Y. Kwon, W. Li, and M. Ghovanloo, “A power-efficient switchedcapacitor
stimulating system for electrical/optical deep brain stimulation,” IEEE
Journal of Solid-State Circuits, vol. 50, no. 1, pp. 360–374, 2014.
[3] Z. Chen, X. Liu, and Z. Wang, “A charge balancing technique for neurostimulators,”
Analog Integrated Circuits and Signal Processing, vol. 105, pp. 483–496, 2020.
[4] N. Butz, A. Taschwer, S. Nessler, Y. Manoli, and M. Kuhl, “A 22 v compliant
56μw twin-track active charge balancing enabling 100% charge compensation even
in monophasic and 36% amplitude correction in biphasic neural stimulators,” IEEE
Journal of Solid-State Circuits, vol. 53, no. 8, pp. 2298–2310, 2018.
[5] P. Kwan and M. J. Brodie, “Early identification of refractory epilepsy,” New England
Journal of Medicine, vol. 342, no. 5, pp. 314–319, 2000.
[6] Y. Temel, A. F. Leentjens, R. M. de Bie, S. Chabardes, and A. Fasano, Fundamentals
and Clinics of Deep Brain Stimulation: An Interdisciplinary Approach. Springer,
2020.
[7] J. S. Perlmutter and J. W. Mink, “Deep brain stimulation,” Annu. Rev. Neurosci.,
vol. 29, pp. 229–257, 2006.
[8] S. Miocinovic, S. Somayajula, S. Chitnis, and J. L. Vitek, “History, applications, and
mechanisms of deep brain stimulation,” JAMA neurology, vol. 70, no. 2, pp. 163–
171, 2013.
[9] H.-T. Tseng, Y.-T. Hsiao, P.-L. Yi, and F.-C. Chang, “Deep brain stimulation increases
seizure threshold by altering rem sleep and delta powers during nrem sleep,”
Frontiers in Neurology, vol. 11, p. 752, 2020.
[10] S. Breit, J. B. Schulz, and A.-L. Benabid, “Deep brain stimulation,” Cell and tissue
research, vol. 318, pp. 275–288, 2004.
[11] T. M. Herrington, J. J. Cheng, and E. N. Eskandar, “Mechanisms of deep brain stimulation,”
Journal of neurophysiology, vol. 115, no. 1, pp. 19–38, 2016.
[12] C. O. Oluigbo, A. Salma, and A. R. Rezai, “Deep brain stimulation for neurological
disorders,” IEEE reviews in biomedical engineering, vol. 5, pp. 88–99, 2012.
[13] A. L. Benabid, “Deep brain stimulation for parkinson＇s disease,” Current opinion
in neurobiology, vol. 13, no. 6, pp. 696–706, 2003.
[14] H. S. Mayberg, A. M. Lozano, V. Voon, H. E. McNeely, D. Seminowicz, C. Hamani,
J. M. Schwalb, and S. H. Kennedy, “Deep brain stimulation for treatment-resistant
depression,” Neuron, vol. 45, no. 5, pp. 651–660, 2005.
[15] S. Kisely, A. Li, N. Warren, and D. Siskind, “A systematic review and meta-analysis
of deep brain stimulation for depression,” Depression and anxiety, vol. 35, no. 5,
pp. 468–480, 2018.
[16] S. H. Kennedy, P. Giacobbe, S. J. Rizvi, F. M. Placenza, Y. Nishikawa, H. S. Mayberg,
and A. M. Lozano, “Deep brain stimulation for treatment-resistant depression:
follow-up after 3 to 6 years,” American Journal of Psychiatry, vol. 168, no. 5,
pp. 502–510, 2011.
[17] F. Syeda, D. Kumbhare, M. Baron, and R. Hadimani, “Modeling of transcranial magnetic stimulation versus pallidal deep brain stimulation for parkinson＇s disease,”
IEEE Transactions on Magnetics, vol. 55, no. 7, pp. 1–5, 2019.
[18] R. Q. So, G. C. McConnell, A. T. August, and W. M. Grill, “Characterizing effects of subthalamic nucleus deep brain stimulation on methamphetamine-induced circling
behavior in hemi-parkinsonian rats,” IEEE Transactions on Neural Systems and Rehabilitation
Engineering, vol. 20, no. 5, pp. 626–635, 2012.
[19] D. Soeiro, “On artificial and animal electricity: Alessandro volta vs. luigi galvani,”
Journal of Neuroscience, vol. 110, no. 3-4, pp. 147–157, 2013.
[20] H.-J. Yoo and C. Van Hoof, Bio-Medical CMOS ICs. Springer, 2010.
[21] P. J. Basser and B. J. Roth, “New currents in electrical stimulation of excitable tissues,”
Annual review of biomedical engineering, vol. 2, no. 1, pp. 377–397, 2000.
[22] O. H. Schmitt and F. O. Schmitt, “A universal precision stimulator,” Science, vol. 76,no. 1971, pp. 328–330, 1932.
[23] E. Noorsal, K. Sooksood, H. Xu, R. Hornig, J. Becker, and M. Ortmanns, “A neural
stimulator frontend with high-voltage compliance and programmable pulse shape for
epiretinal implants,” IEEE Journal of Solid-State Circuits, vol. 47, no. 1, pp. 244–
256, 2011.
[24] L. Yao and M. Je, “Cmos based 16-channel neural/muscular stimulation system with
arbitrary waveform and active charge balancing circuit,” in 2011 International Symposium
on Integrated Circuits, pp. 456–459, IEEE, 2011.
[25] D. T. Plachta, M. Gierthmuehlen, O. Cota, F. Boeser, and T. Stieglitz, “Baroloop:
Using a multichannel cuff electrode and selective stimulation to reduce blood pressure,”
in 2013 35th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society (EMBC), pp. 755–758, IEEE, 2013.
[26] H.-M. Lee, H. Park, and M. Ghovanloo, “A power-efficient wireless system with
adaptive supply control for deep brain stimulation,” IEEE journal of solid-state circuits,
vol. 48, no. 9, pp. 2203–2216, 2013.
[27] R. Ranjandish and O. Shoaei, “A simple and precise charge balancing method for
voltage mode stimulation,” in 2014 IEEE Biomedical Circuits and Systems Conference
(BioCAS) Proceedings, pp. 376–379, 2014.
[28] S. Ha, C. Kim, J. Park, G. Cauwenberghs, and P. P. Mercier, “A fully integrated rfpowered
energy-replenishing current-controlled stimulator,” IEEE Transactions on
Biomedical Circuits and Systems, vol. 13, no. 1, pp. 191–202, 2018.
[29] Z. Luo and M.-D. Ker, “A high-voltage-tolerant and precise charge-balanced neurostimulator
in low voltage cmos process,” IEEE transactions on biomedical circuits
and systems, vol. 10, no. 6, pp. 1087–1099, 2016.
[30] C.-W. Liu, Y.-L. Chen, P.-C. Liao, S.-P. Lin, T.-W. Wang, M.-J. Chung, P.-H. Chen,
M.-D. Ker, and C.-Y. Wu, “An 82.9%-efficiency triple-output battery management
unit for implantable neuron stimulator in 180-nm standard cmos,” IEEE Transactions
on Circuits and Systems II: Express Briefs, vol. 66, no. 5, pp. 788–792, 2019.
[31] D. Rozgić, V. Hokhikyan, W. Jiang, I. Akita, S. Basir-Kazeruni, H. Chandrakumar,
and D. Marković, “A 0.338cm3, artifact-free, 64-contact neuromodulation platform
for simultaneous stimulation and sensing,” IEEE transactions on biomedical circuits
and systems, vol. 13, no. 1, pp. 38–55, 2018.
[32] J. Vidal and M. Ghovanloo, “Towards a switched-capacitor based stimulator for efficient
deep-brain stimulation,” in 2010 Annual International Conference of the IEEE
Engineering in Medicine and Biology, pp. 2927–2930, IEEE, 2010.
[33] C.-C. Hsieh and M.-D. Ker, “Monopolar biphasic stimulator with discharge function
and negative level shifter for neuromodulation soc integration in low-voltage cmos
process,” IEEE Transactions on Biomedical Circuits and Systems, vol. 15, no. 3,
pp. 568–579, 2021.
[34] D. Jiang, Y. Wu, N. Almarri, M. Habibollahi, F. Liu, J. B. Bryson, L. Greensmith,
and A. Demosthenous, “An integrated bidirectional multi-channel opto-electro arbitrary
waveform stimulator for treating motor neurone disease,” in 2021 IEEE International
Symposium on Circuits and Systems (ISCAS), pp. 1–4, IEEE, 2021.
[35] H. Pu, O. Malekzadeh-Arasteh, A. R. Danesh, Z. Nenadic, A. H. Do, and P. Heydari,
“A cmos dual-mode brain–computer interface chipset with 2-mv precision timebased
charge balancing and stimulation-side artifact suppression,” IEEE Journal of
Solid-State Circuits, vol. 57, no. 6, pp. 1824–1840, 2021.
[36] J. V. Shah, C. J. Quinkert, B. J. Collar, M. T. Williams, E. N. Biggs, and P. P. Irazoqui,
“A highly miniaturized, chronically implanted asic for electrical nerve stimulation,”
IEEE Transactions on Biomedical Circuits and Systems, vol. 16, no. 2, pp. 233–243,
2022.
[37] B. Nath, S.-Y. Peng, Z.-J. Lo, Y.-H. Pai, Y.-T. Yeh, H.-H. Chang, Y.-C. Lu, S.-H.
Huang, and F.-C. Chang, “A biphasic current-mode stimulator integrated circuit with
a novel residual charge compensation mechanism,” Integration, vol. 91, pp. 79–88,
2023.
[38] S. F. Lempka, B. Howell, K. Gunalan, A. G. Machado, and C. C. McIntyre, “Characterization
of the stimulus waveforms generated by implantable pulse generators
for deep brain stimulation,” Clinical Neurophysiology, vol. 129, no. 4, pp. 731–742,
2018.
[39] A. Wongsarnpigoon, J. P. Woock, and W. M. Grill, “Efficiency analysis of waveform
shape for electrical excitation of nerve fibers,” IEEE Transactions on Neural Systems
and Rehabilitation Engineering, vol. 18, no. 3, pp. 319–328, 2010.
[40] C. J. Anderson, D. N. Anderson, S. M. Pulst, C. R. Butson, and A. D. Dorval, “Neural
selectivity, efficiency, and dose equivalence in deep brain stimulation through pulse
width tuning and segmented electrodes,” Brain stimulation, vol. 13, no. 4, pp. 1040–
1050, 2020.
[41] M. G. Fishler, “Theoretical predictions of the optimal monophasic and biphasic defibrillation
waveshapes,” IEEE transactions on biomedical engineering, vol. 47,
no. 1, pp. 59–67, 2000.
[42] S. Jezernik and M. Morari, “Energy-optimal electrical excitation of nerve fibers,”
IEEE Transactions on Biomedical Engineering, vol. 52, no. 4, pp. 740–743, 2005.
[43] C. R. Butson and C. C. McIntyre, “Differences among implanted pulse generator
waveforms cause variations in the neural response to deep brain stimulation,” Clinical
neurophysiology, vol. 118, no. 8, pp. 1889–1894, 2007.
[44] Y.-K. Lo, R. Hill, K. Chen, and W. Liu, “Precision control of pulse widths for
charge balancing in functional electrical stimulation,” in 2013 6th International
IEEE/EMBS Conference on Neural Engineering (NER), pp. 1481–1484, 2013.
[45] J.-J. Sit and R. Sarpeshkar, “A low-power blocking-capacitor-free charge-balanced
electrode-stimulator chip with less than 6 na dc error for 1-ma full-scale stimulation,”
IEEE Transactions on Biomedical Circuits and Systems, vol. 1, no. 3, pp. 172–183,
2007.
[46] K. Sooksood, T. Stieglitz, and M. Ortmanns, “An experimental study on passive
charge balancing,” Advances in Radio Science, vol. 7, pp. 197–200, 2009.
[47] K. Song, H. Lee, S. Hong, H. Cho, U. Ha, and H.-J. Yoo, “A sub-10 na dc-balanced
adaptive stimulator ic with multi-modal sensor for compact electro-acupuncture
stimulation,” IEEE Transactions on Biomedical Circuits and Systems, vol. 6, no. 6,
pp. 533–541, 2012.
[48] M. O’Halloran and R. Sarpeshkar, “A 10-nw 12-bit accurate analog storage cell with
10-aa leakage,” IEEE journal of solid-state circuits, vol. 39, no. 11, pp. 1985–1996,
2004.
[49] X. Liu, A. Demosthenous, and N. Donaldson, “An integrated implantable stimulator
that is fail-safe without off-chip blocking-capacitors,” IEEE Transactions on
Biomedical Circuits and Systems, vol. 2, no. 3, pp. 231–244, 2008.
[50] H. Chun, Y. Yang, and T. Lehmann, “Safety ensuring retinal prosthesis with precise
charge balance and low power consumption,” IEEE transactions on biomedical
circuits and systems, vol. 8, no. 1, pp. 108–118, 2013.
[51] W.-Y. Hsu and A. Schmid, “Compact, energy-efficient high-frequency switched
capacitor neural stimulator with active charge balancing,” IEEE transactions on
biomedical circuits and systems, vol. 11, no. 4, pp. 878–888, 2017.
[52] K. Eom, H.-S. Lee, M. Park, S. M. Yang, J. C. Choe, S.-W. Hwang, Y.-W. Suh,
and H.-M. Lee, “A 92%-efficiency inductor-charging switched-capacitor stimulation
system with level-adaptive duty modulation and offset charge balancing for muscular
stimulation,” in 2022 IEEE Custom Integrated Circuits Conference (CICC), pp. 1–2,
IEEE, 2022.
[53] Y. Jia, U. Guler, Y.-P. Lai, Y. Gong, A. Weber, W. Li, and M. Ghovanloo, “A trimodal
wireless implantable neural interface system-on-chip,” IEEE transactions on
biomedical circuits and systems, vol. 14, no. 6, pp. 1207–1217, 2020.
[54] N. Butz, U. Kalita, and Y. Manoli, “Active charge balancer with adaptive 3.3 v to
38 v supply compliance for neural stimulators,” IEEE Transactions on Circuits and
Systems I: Regular Papers, vol. 68, no. 10, pp. 4013–4024, 2021.
[55] R. Shulyzki, K. Abdelhalim, A. Bagheri, M. T. Salam, C. M. Florez, J. L. P. Velazquez,
P. L. Carlen, and R. Genov, “320-channel active probe for high-resolution
neuromonitoring and responsive neurostimulation,” IEEE transactions on biomedical
circuits and systems, vol. 9, no. 1, pp. 34–49, 2014.
[56] M. Ortmanns, A. Rocke, M. Gehrke, and H.-J. Tiedtke, “A 232-channel epiretinal
stimulator asic,” IEEE Journal of Solid-State Circuits, vol. 42, no. 12, pp. 2946–
2959, 2007.
[57] K. Sooksood, T. Stieglitz, and M. Ortmanns, “An active approach for charge balancing
in functional electrical stimulation,” IEEE Transactions on Biomedical Circuits
and Systems, vol. 4, no. 3, pp. 162–170, 2010.
[58] L. Yao, P. Li, and M. Je, “A pulse-width-adaptive active charge balancing circuit with
pulse-insertion based residual charge compensation and quantization for electrical
stimulation applications,” in 2015 IEEE Asian solid-state circuits conference (ASSCC),
pp. 1–4, IEEE, 2015.
[59] J.-Y. Son and H.-K. Cha, “An implantable neural stimulator ic with anodic current
pulse modulation based active charge balancing,” IEEE Access, vol. 8, pp. 136449–
136458, 2020.
[60] H. A. Yiğit, H. Uluşan, M. Koç, M. B. Yüksel, S. Chamanian, and H. Külah, “Single
supply pwm fully implantable cochlear implant interface circuit with active charge
balancing,” IEEE Access, vol. 9, pp. 52642–52653, 2021.
[61] M. Parastarfeizabadi and A. Z. Kouzani, “Advances in closed-loop deep brain stimulation
devices,” Journal of neuroengineering and rehabilitation, vol. 14, no. 1, pp. 1–
20, 2017.
[62] Y.H. Pai, "Integrated Circuit Design of Multi-Channel Programmable Biphasic Current-
Mode Stimulator System-On-Chip and Single-Inductor-Dual-Output Buck Converter
for Functional Electrical Stimulation," Master Thesis, National Taiwan University of
Science and Technology, 2022.
[63] W. Bouthour, P. Mégevand, J. Donoghue, C. Lüscher, N. Birbaumer, and P. Krack,
“Biomarkers for closed-loop deep brain stimulation in parkinson disease and beyond,”
Nature Reviews Neurology, vol. 15, no. 6, pp. 343–352, 2019.
[64] A. Velisar, J. Syrkin-Nikolau, Z. Blumenfeld, M. Trager, M. Afzal, V. Prabhakar,
and H. Bronte-Stewart, “Dual threshold neural closed loop deep brain stimulation in
parkinson disease patients,” Brain stimulation, vol. 12, no. 4, pp. 868–876, 2019.
[65] L. A. Johnson, S. D. Nebeck, A. Muralidharan, M. D. Johnson, K. B. Baker, and J. L.
Vitek, “Closed-loop deep brain stimulation effects on parkinsonian motor symptoms
in a non-human primate–is beta enough?,” Brain stimulation, vol. 9, no. 6, pp. 892–
896, 2016.
[66] J. Prosky, J. Cagle, K. K. Sellers, R. Gilron, C. de Hemptinne, A. Schmitgen, P. A.
Starr, E. F. Chang, and P. Shirvalkar, “Practical closed-loop strategies for deep brain
stimulation: Lessons from chronic pain,” Frontiers in Neuroscience, p. 1733, 2021.
[67] M. K. Hosain, A. Kouzani, and S. Tye, “Closed loop deep brain stimulation: an
evolving technology,” Australasian physical & engineering sciences in medicine,
vol. 37, pp. 619–634, 2014.
[68] B. Johnsen, J. Jeppesen, and C. H. V. Duez, “Common patterns of eeg reactivity
in post-anoxic coma identified by quantitative analyses,” Clinical Neurophysiology,
vol. 142, pp. 143–153, 2022.
[69] S. Shorvon, E. Perucca, and J. Engel Jr, The treatment of epilepsy. John Wiley &
Sons, 2015.
[70] B. Hunyadi, A. Siekierska, J. Sourbron, D. Copmans, and P. A. de Witte, “Automated
analysis of brain activity for seizure detection in zebrafish models of epilepsy,” Journal
of Neuroscience Methods, vol. 287, pp. 13–24, 2017.
[71] M. Liotti, S. R. Pliszka, R. Perez, D. Kothmann, and M. G. Woldorff, “Abnormal
brain activity related to performance monitoring and error detection in children with
adhd,” Cortex, vol. 41, no. 3, pp. 377–388, 2005.
[72] A. J. Rissling, S. Makeig, D. L. Braff, and G. A. Light, “Neurophysiologic markers
of abnormal brain activity in schizophrenia,” Current psychiatry reports, vol. 12,
pp. 572–578, 2010.
[73] J. A. Wilden and A. A. Cohen-Gadol, “Evaluation of first nonfebrile seizures,” American
family physician, vol. 86, no. 4, pp. 334–340, 2012.
[74] H. Blumenfeld and J. Taylor, “Why do seizures cause loss of consciousness?,” The
Neuroscientist, vol. 9, no. 5, pp. 301–310, 2003.
[75] H. Liu, W. Li, M. Zhao, J. Wu, J. Wu, J. Yang, and B. Jiao, “Altered temporal dynamics
of brain activity in patients with generalized tonic-clonic seizures,” PLoS One,
vol. 14, no. 7, p. e0219904, 2019.
[76] J. R. Hughes and M. Melyn, “Eeg and seizures in autistic children and adolescents:
further findings with therapeutic implications,” Clinical EEG and Neuroscience,
vol. 36, no. 1, pp. 15–20, 2005.
[77] A. Wongsarnpigoon and W. M. Grill, “Energy-efficient waveform shapes for neural
stimulation revealed with a genetic algorithm,” Journal of neural engineering, vol. 7,
no. 4, p. 046009, 2010.
[78] W. M. Grill, “Model-based analysis and design of waveforms for efficient neural
stimulation,” Progress in brain research, vol. 222, pp. 147–162, 2015.
[79] S. Eickhoff and J. C. Jarvis, “An investigation of neural stimulation efficiency with
gaussian waveforms,” IEEE Transactions on Neural Systems and Rehabilitation Engineering,
vol. 28, no. 1, pp. 104–112, 2019.
[80] T. J. Foutz and C. C. McIntyre, “Evaluation of novel stimulus waveforms for deep
brain stimulation,” Journal of neural engineering, vol. 7, no. 6, p. 066008, 2010.
[81] M. Yip, P. Bowers, V. Noel, A. Chandrakasan, and K. M. Stankovic, “Energyefficient
waveform for electrical stimulation of the cochlear nerve,” Scientific Reports,
vol. 7, no. 1, p. 13582, 2017.
[82] P. Kosta, K. Loizos, and G. Lazzi, “Stimulus waveform design for decreasing charge
and increasing stimulation selectivity in retinal prostheses,” Healthcare Technology
Letters, vol. 7, no. 3, pp. 66–71, 2020.
[83] S. K. Tan, R. Vlamings, L. Lim, T. Sesia, M. L. Janssen, H. W. Steinbusch, V. Visser-
Vandewalle, and Y. Temel, “Experimental deep brain stimulation in animal models,”
Neurosurgery, vol. 67, no. 4, pp. 1073–1080, 2010.
[84] S. Desmoulin-Canselier and B. Moutaud, “Animal models and animal experimentation
in the development of deep brain stimulation: From a specific controversy to a
multidimensional debate,” Frontiers in neuroanatomy, p. 51, 2019.
[85] T. Wyckhuys, P.-J. Geerts, R. Raedt, K. Vonck, W. Wadman, P. Boon, et al., “Deep
brain stimulation for epilepsy: knowledge gained from experimental animal models,”
Acta Neurol Belg, vol. 109, no. 2, pp. 63–80, 2009.
[86] Y. Temel, “Deep brain stimulation in animal models,” Handbook of Clinical Neurology,
vol. 116, pp. 19–25, 2013.
[87] C.-C. Chiang, T. P. Ladas, L. E. Gonzalez-Reyes, and D. M. Durand, “Seizure suppression
by high frequency optogenetic stimulation using in vitro and in vivo animal
models of epilepsy,” Brain stimulation, vol. 7, no. 6, pp. 890–899, 2014.
[88] A. Dhir, “Pentylenetetrazol (ptz) kindling model of epilepsy,” Current protocols in
neuroscience, vol. 58, no. 1, pp. 9–37, 2012.
122