This research investigates the use of conducting polymer coatings on platinum (Pt) electrodes for use in neuroprostheses. Conducting polymers aim to provide an environment conducive to neurite outgrowth and attachment at the electrode sites, producing intimate contact between neural cells and stimulating electrodes. Conducting polymers were electropolymerised onto model Pt electrodes. Conventional polymers polypyrrole (PPy) and poly-3,4-ethylenedioxythiphene (PEDOT) doped with polystyrenesulfonate (PSS) and para-toluenesulfonate (pTS)were investigated. Improvement of material properties was assessed through the layering of polymers with multi-walled carbon nanotubes (MWNTs). The ability to incorporate cell attachment bioactivity into polymers was examined through the doping of PEDOT with anionic laminin peptides DCDPGYIGSR and DEDEDYFQRYLI. Finally, nerve growth factor (NGF), was entrapped in PEDOT during polymerisation and tested for neurite outgrowth bioactivity against the PC12 cell line. Each polymer modification was assessed for electrical performance over multiple reduction-oxidation cycles, conductivity and impedance spectroscopy, mechanical adherence and hardness, and biological response. Scanning electron microscopy was used to visualise film topography and x-ray photon spectroscopy was employed to examine chemical constitution of the polymers. For application of electrode coatings to neural prostheses, optimal bioactive conducting polymer PEDOT/pTS/NGF was deposited on electrode arrays intended for implantation. PC12s were used to assess the bioactivity of NGF functionalised PEDOT when electrode size was micronised. Flexibility of the design was tested by tailoring PEDOT bioactivity for the cloned retinal ganglion cell, RGC-5, differentiated via staurasporine. It was established that PEDOT films had superior electrical and cell growth characteristics, but only PPy was able to benefit from incorporation of MWNTs. Bioactive polymers were produced through inclusion of both laminin peptides and NGF, but the optimum film constitution was found to be PEDOT doped with pTS with NGF entrapped during electrodeposition. Application of this polymer to an implant device was confirmed through positive neurite outgrowth on vision prosthesis electrode arrays. The design was shown to be flexible when tailored for RGC-5s, with differentiation occurring on both PEDOT/pTS and PEDOT/DEDEDYFQRYLI. Conducting polymers demonstrate the potential to improve electrode-cell interactions. Future work will focus on the effect of electrical stimulation and design of bioactive polymers with improved cell attachment properties.

This research investigates the use of conducting polymer coatings on platinum (Pt) electrodes for use in neuroprostheses. Conducting polymers aim to provide an environment conducive to neurite outgrowth and attachment at the electrode sites, producing intimate contact between neural cells and stimulating electrodes. Conducting polymers were electropolymerised onto model Pt electrodes. Conventional polymers polypyrrole (PPy) and poly-3,4-ethylenedioxythiphene (PEDOT) doped with polystyrenesulfonate (PSS) and para-toluenesulfonate (pTS)were investigated. Improvement of material properties was assessed through the layering of polymers with multi-walled carbon nanotubes (MWNTs). The ability to incorporate cell attachment bioactivity into polymers was examined through the doping of PEDOT with anionic laminin peptides DCDPGYIGSR and DEDEDYFQRYLI. Finally, nerve growth factor (NGF), was entrapped in PEDOT during polymerisation and tested for neurite outgrowth bioactivity against the PC12 cell line. Each polymer modification was assessed for electrical performance over multiple reduction-oxidation cycles, conductivity and impedance spectroscopy, mechanical adherence and hardness, and biological response. Scanning electron microscopy was used to visualise film topography and x-ray photon spectroscopy was employed to examine chemical constitution of the polymers. For application of electrode coatings to neural prostheses, optimal bioactive conducting polymer PEDOT/pTS/NGF was deposited on electrode arrays intended for implantation. PC12s were used to assess the bioactivity of NGF functionalised PEDOT when electrode size was micronised. Flexibility of the design was tested by tailoring PEDOT bioactivity for the cloned retinal ganglion cell, RGC-5, differentiated via staurasporine. It was established that PEDOT films had superior electrical and cell growth characteristics, but only PPy was able to benefit from incorporation of MWNTs. Bioactive polymers were produced through inclusion of both laminin peptides and NGF, but the optimum film constitution was found to be PEDOT doped with pTS with NGF entrapped during electrodeposition. Application of this polymer to an implant device was confirmed through positive neurite outgrowth on vision prosthesis electrode arrays. The design was shown to be flexible when tailored for RGC-5s, with differentiation occurring on both PEDOT/pTS and PEDOT/DEDEDYFQRYLI. Conducting polymers demonstrate the potential to improve electrode-cell interactions. Future work will focus on the effect of electrical stimulation and design of bioactive polymers with improved cell attachment properties.

Currently there does not exist a type of peripheral nerve interface that adequately combines spatial selectivity, spatial coverage and low invasiveness. In order to address this lack, we investigated the application of bioelectric source localization algorithms, adapted from electroencephalography/magnetoencephalography, to recordings from a 56-contact “matrix” nerve cuff electrode. If successful, this strategy would enable us to improve current neuroprostheses and conduct more detailed investigations of neural control systems. Using forward field similarities, we first developed a method to reduce the number of unnecessary variables in the inverse problem, and in doing so obtained an upper bound on the spatial resolution. Next, a simulation study of the peripheral nerve source localization problem revealed that the method is unlikely to work unless noise is very low and a very accurate model of the nerve is available. Under more realistic conditions, the method had localization errors in the 140 μm-180 μm range, high numbers of spurious pathways, and low resolution. On the other hand, the simulations also showed that imposing physiologically meaningful constraints on the solution can reduce the number of spurious pathways. Both the influence of the constraints and the importance of the model accuracy were validated experimentally using recordings from rat sciatic nerves. Unfortunately, neither idealized models nor models based on nerve sample cross-sections were sufficiently accurate to allow reliable identification of the branches stimulated during the experiments. To overcome this problem, an experimental leadfield was constructed using training data, thereby eliminating the dependence on anatomical models. This new strategy was successful in identifying single-branch cases, but not multi-branches ones. Lastly, an examination of the information contained in the matrix cuff recordings was performed in comparison to a single-ring configuration of contacts. The matrix cuff was able to achieve better fascicle discrimination due to its ability to select among the most informative locations around the nerve. These findings suggest that nerve cuff-based neuroprosthetic applications would benefit from implanting devices with a large number of contacts, then performing a contact selection procedure. Conditions that must be met before source localization approaches can be applied in practice to peripheral nerves were also discussed.

Currently there does not exist a type of peripheral nerve interface that adequately combines spatial selectivity, spatial coverage and low invasiveness. In order to address this lack, we investigated the application of bioelectric source localization algorithms, adapted from electroencephalography/magnetoencephalography, to recordings from a 56-contact “matrix” nerve cuff electrode. If successful, this strategy would enable us to improve current neuroprostheses and conduct more detailed investigations of neural control systems. Using forward field similarities, we first developed a method to reduce the number of unnecessary variables in the inverse problem, and in doing so obtained an upper bound on the spatial resolution. Next, a simulation study of the peripheral nerve source localization problem revealed that the method is unlikely to work unless noise is very low and a very accurate model of the nerve is available. Under more realistic conditions, the method had localization errors in the 140 μm-180 μm range, high numbers of spurious pathways, and low resolution. On the other hand, the simulations also showed that imposing physiologically meaningful constraints on the solution can reduce the number of spurious pathways. Both the influence of the constraints and the importance of the model accuracy were validated experimentally using recordings from rat sciatic nerves. Unfortunately, neither idealized models nor models based on nerve sample cross-sections were sufficiently accurate to allow reliable identification of the branches stimulated during the experiments. To overcome this problem, an experimental leadfield was constructed using training data, thereby eliminating the dependence on anatomical models. This new strategy was successful in identifying single-branch cases, but not multi-branches ones. Lastly, an examination of the information contained in the matrix cuff recordings was performed in comparison to a single-ring configuration of contacts. The matrix cuff was able to achieve better fascicle discrimination due to its ability to select among the most informative locations around the nerve. These findings suggest that nerve cuff-based neuroprosthetic applications would benefit from implanting devices with a large number of contacts, then performing a contact selection procedure. Conditions that must be met before source localization approaches can be applied in practice to peripheral nerves were also discussed.

abstract: Brain micromotion is a phenomenon that arises from basic physiological functions such as respiration (breathing) and vascular pulsation (pumping blood or heart rate). These physiological processes cause small micro displacements of 2-4µm for vascular pulsation and 10-30µm for respiration, in rat models. One problem related to micromotion is the instability of the probe and its ability to acquire stable neural recordings in chronic studies. It has long been thought the membrane potential (MP) changes due to micromotion in the presence of brain implants were an artefact caused by the implant. Here is shown that intracellular membrane potential changes are a consequence of the activation of mechanosensitive ion channels at the neural interface. A combination of aplysia and rat animal models were used to show activation of mechanosensitive ion channels is occurring during a neural recording. During simulated micromotion of displacements of 50μm and 100μm at a frequency of 1 Hz, showed a change of 8 and 10mV respectively and that the addition of Ethylenediaminetetraacetic acid (EDTA) inhibited the membrane potential changes. The application of EDTA showed a 71% decrease in changes in membrane potential changes due to micromotion. Simulation of breathing using periodic motion of a probe in an Aplysia model showed that there were no membrane potential changes for <1.5kPa and action potentials were observed at >3.1kPa. Drug studies utilizing 5-HT showed an 80% reduction in membrane potentials. To validate the electrophysiological changes due to micromotion in a rat model, a double barrel pipette for simultaneous recording and drug delivery was designed, the drug delivery tip was recessed from the recording tip no greater than 50μm on average. The double barrel pipette using iontophoresis was used to deliver 30 μM of Gadolinium Chloride (Gd3+) into the microenvironment of the cell. Here is shown a significant reduction in membrane potential for n = 13 cells across 4 different rats tested using Gd3+. Membrane potential changes related to breathing and vascular pulsation were reduced between approximately 0.25-2.5 mV for both breathing and heart rate after the addition of Gd3+, a known mechanosensitive ion channel blocker. / Dissertation/Thesis / Masters Thesis Biomedical Engineering 2020

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O registro de sinais neurais através de matrizes de microeletrodos implantáveis no meio extracelular do córtex cerebral tem-se tornado um paradigma experimental para a neurociência. Por outro lado, a pesquisa recente sobre neuropróteses motoras tem mostrado que é possível decodificar comandos motores a partir dos sinais registrados no meio extracelular do córtex cerebral. Em ambos os contextos, neurociência experimental e desenvolvimento de neuropróteses motoras, um dos aspectos encontrados no estado da arte ´e a utilização de circuitos integrados (chips) implantados no cérebro. Nesses chips, os sinais neurais medidos com os microeletrodos são amplificados, filtrados, processados e transmitidos a um computador externo mediante fios que atravessam o crânio. Existe o interesse em desenvolver chips implantáveis que transmitam os sinais ao computador externo sem a necessidade de fios que atravessem o crânio. Na pesquisa do estado da arte tem-se encontrado a utilização de tais chips implantáveis sem fio em ratos e macacos, porém até a data da elaboração deste texto não foram encontrados relatos da aplicação em humanos. Um dos aspectos que deve se levar em consideração no desenvolvimento de interfaces neurais implantáveis sem fio é a largura de banda do canal de comunicação. Quanto maior a quantidade de dados a serem transmitidos, maior a largura de banda necessária e maior o aquecimento do chip devido à dissipação de potência. Esta tese aborda sistemas de processamento de sinais neurais extracelulares que tem como objetivo reduzir a quantidade de dados a serem transmitidos e assim viabilizar a transmissão sem fio. Para poder ser integrados dentro do chip implantável, esses sistemas de processamento devem estar otimizados em termos de área e consumo de potência. Dois processamentos encontrados na pesquisa de interfaces neurais implantáveis são a detecção de impulsos elétricos e a separação de impulsos elétricos (Spike Sorting). Nesta tese apresentam-se soluções para esses tipos de processamentos visando a implementação mediante tecnologia CMOS (Complementary Metal Oxide Semiconductor). Para o caso da detecção de impulsos elétricos (spikes), nesta tese apresenta-se uma alternativa de implementação em hardware de um operador matemático conhecido como operador não linear de energia (NEO do inglês Nonlinear Energy Operator) ou operador Teager. Através da aplicação desse operador a um sinal neural evidencia-se a presença de spikes e atenua-se o ruído. Uma das características inovadoras da implementação apresentada nesta tese é a utilização de um circuito elevador ao quadrado que consiste de apenas três transistores, como bloco funcional básico para a realização da operação NEO. O circuito NEO desenvolvido consome 300 pJ no processamento de um spike e foi caracterizado por simulação até em 30 kHz, frequência que é compatível com as taxas de amostragem encontradas na literatura. O outro processamento abordado nesta tese, conhecido como separação de impulsos elétricos ou Spike Sorting, consiste no agrupamento dos impulsos elétricos registrados por um eletrodo em categorias, de maneira que em uma categoria estejam os impulsos gerados por um mesmo neurônio. Em outras palavras, o objetivo é reconhecer quais dos impulsos elétricos medidos pelo eletrodo pertencem a um mesmo neurônio, sendo possível que vários neurônios influenciem na medida realizada por um único eletrodo. Uma solução para a separação de impulsos, apropriada no contexto de sistemas implantáveis, é o template matching. Essa técnica baseia-se na geração de modelos (templates) durante uma fase inicial ao final da qual o número de templates gerados corresponde ao número de neurônios identificados pelo eletrodo. Numa fase seguinte, o sistema associa cada impulso elétrico detectado a um dos modelos inicialmente gerados. Nesta tese propõe-se um sistema de classificação que executa essa segunda fase do processo de spike sorting. Nesta tese apresenta-se o projeto de um sistema de classificação de spikes baseado na t écnica template matching, implementado com tecnologia CMOS. A implementação proposta nesta tese baseia-se na representação de amostras analógicas mediante o tempo. Esse tipo de representação de sinais analógicos mediante atrasos de pulsos digitais está sendo muito utilizado como alternativa à representação no domínio da tensão, da corrente ou da carga elétrica. A vantagem desse tipo de representação é que não se vê severamente afetada pela redução da tensão de alimentação dos circuitos integrados fabricados em tecnologias submicrométricas. A taxa de acerto na classificação do sistema desenvolvido é maior que 99% inclusive considerando um offset de até 20mV no comparador de saída. Os circuitos apresentados neste trabalho foram projetados considerando dispositivos da tecnologia TSMC de 90nm. / Neural signals recording through implantable microelectrode arrays in cortex extracellular medium has become an experimental paradigm for neuroscience. Moreover, recent research about motor neuroprostheses has shown that it is possible to decode motor commands from the signals recorded in the cerebral cortex extracellular medium. In both situations, experimental neuroscience and motor neuroprostheses development, one of the issues encountered in the state-of-the-art is the use of integrated circuits (chips) implanted in the brain. In these chips, neural signals measured with microelectrodes are amplified, filtered, processed, and transmitted to an external computer through wires that run through the skull. There is interest in developing implantable chips that transmit signals to the external computer without the need for wires passing through the skull. In the survey of the state-of-the-art it has found the use of such implantable wireless chips in rats and monkeys, but until the date of this writing we have not found reports of application in humans. One of the aspects that must be taken into account in the development of wireless implantable neural interfaces is the bandwidth of the communication channel. The greater the amount of data to be transmitted, the greater the bandwidth required and higher chip heating due to power dissipation. This thesis deals with extracellular neural signals processing systems that aim to reduce the amount of data to be transmitted and in this way to enable wireless transmission. In order to integrate them into an implantable chip, those processing systems must be optimized in terms of area and power consumption. Two processes found in the research of implantable neural interfaces are spike detection and spike sorting. In this thesis solutions for these types of processing are presented considering their implementation by CMOS (Complementary Metal Oxide Semiconductor). For the case of spike detection in this thesis it is presented an alternative for the hardware implementation of a mathematical operator known as NEO (Nonlinear Energy Operator). Through the application of this operator to a neural signal the presence of spikes becomes evident and the noise is attenuated. One of the innovative characteristics of the implementation presented in this thesis is the use of a squarer circuit which consists of only three transistors, as a basic function block for performing operation of NEO. NEO circuit consumes 300 pJ in processing a spike, and was characterized by simulation up to 30 kHz, frequency which is compatible with sampling rates found in the literature. The other processing discussed in this thesis, known as Spike Sorting, is the grouping of electrical impulses recorded by an electrode into categories so that the spikes belonging to the same category were generated by a single neuron. In other words, the goal is to recognize which of the spikes measured by the electrode belong to the same neuron, given that it is possible that several neurons influence the measure performed by a single electrode. A solution for the Spike Sorting suitable in the context of implantable systems, is the template matching. This technique is based on generating templates during an initial phase at the end of which the number of generated templates corresponds to the number of neurons identified by the electrode. In the next phase, the system associates each detected spike to one of the templates generated initially. In this thesis it is proposed a classification systems which performs that second phase of the spike sorting process. This thesis presents the design of a spike classification system based on template matching technique, implemented in CMOS technology. The processing proposed in this work is based on the time-based representation of the analog samples. This kind of representation of analog signals by delays of digital pulses is being widely used as an alternative to the classical representation of samples by voltage, current or electric charge. The advantage of this time-mode representation is that it is not severely affected by reduced supply voltage of integrated circuits manufactured in sub-micrometer technologies. The classification hit rate of the developed system is greater than 99% even when an offset of 20 mV is assumed for the output comparator. All the circuits presented in this work were designed using devices from TSMC 90nm technology.

O registro de sinais neurais através de matrizes de microeletrodos implantáveis no meio extracelular do córtex cerebral tem-se tornado um paradigma experimental para a neurociência. Por outro lado, a pesquisa recente sobre neuropróteses motoras tem mostrado que é possível decodificar comandos motores a partir dos sinais registrados no meio extracelular do córtex cerebral. Em ambos os contextos, neurociência experimental e desenvolvimento de neuropróteses motoras, um dos aspectos encontrados no estado da arte ´e a utilização de circuitos integrados (chips) implantados no cérebro. Nesses chips, os sinais neurais medidos com os microeletrodos são amplificados, filtrados, processados e transmitidos a um computador externo mediante fios que atravessam o crânio. Existe o interesse em desenvolver chips implantáveis que transmitam os sinais ao computador externo sem a necessidade de fios que atravessem o crânio. Na pesquisa do estado da arte tem-se encontrado a utilização de tais chips implantáveis sem fio em ratos e macacos, porém até a data da elaboração deste texto não foram encontrados relatos da aplicação em humanos. Um dos aspectos que deve se levar em consideração no desenvolvimento de interfaces neurais implantáveis sem fio é a largura de banda do canal de comunicação. Quanto maior a quantidade de dados a serem transmitidos, maior a largura de banda necessária e maior o aquecimento do chip devido à dissipação de potência. Esta tese aborda sistemas de processamento de sinais neurais extracelulares que tem como objetivo reduzir a quantidade de dados a serem transmitidos e assim viabilizar a transmissão sem fio. Para poder ser integrados dentro do chip implantável, esses sistemas de processamento devem estar otimizados em termos de área e consumo de potência. Dois processamentos encontrados na pesquisa de interfaces neurais implantáveis são a detecção de impulsos elétricos e a separação de impulsos elétricos (Spike Sorting). Nesta tese apresentam-se soluções para esses tipos de processamentos visando a implementação mediante tecnologia CMOS (Complementary Metal Oxide Semiconductor). Para o caso da detecção de impulsos elétricos (spikes), nesta tese apresenta-se uma alternativa de implementação em hardware de um operador matemático conhecido como operador não linear de energia (NEO do inglês Nonlinear Energy Operator) ou operador Teager. Através da aplicação desse operador a um sinal neural evidencia-se a presença de spikes e atenua-se o ruído. Uma das características inovadoras da implementação apresentada nesta tese é a utilização de um circuito elevador ao quadrado que consiste de apenas três transistores, como bloco funcional básico para a realização da operação NEO. O circuito NEO desenvolvido consome 300 pJ no processamento de um spike e foi caracterizado por simulação até em 30 kHz, frequência que é compatível com as taxas de amostragem encontradas na literatura. O outro processamento abordado nesta tese, conhecido como separação de impulsos elétricos ou Spike Sorting, consiste no agrupamento dos impulsos elétricos registrados por um eletrodo em categorias, de maneira que em uma categoria estejam os impulsos gerados por um mesmo neurônio. Em outras palavras, o objetivo é reconhecer quais dos impulsos elétricos medidos pelo eletrodo pertencem a um mesmo neurônio, sendo possível que vários neurônios influenciem na medida realizada por um único eletrodo. Uma solução para a separação de impulsos, apropriada no contexto de sistemas implantáveis, é o template matching. Essa técnica baseia-se na geração de modelos (templates) durante uma fase inicial ao final da qual o número de templates gerados corresponde ao número de neurônios identificados pelo eletrodo. Numa fase seguinte, o sistema associa cada impulso elétrico detectado a um dos modelos inicialmente gerados. Nesta tese propõe-se um sistema de classificação que executa essa segunda fase do processo de spike sorting. Nesta tese apresenta-se o projeto de um sistema de classificação de spikes baseado na t écnica template matching, implementado com tecnologia CMOS. A implementação proposta nesta tese baseia-se na representação de amostras analógicas mediante o tempo. Esse tipo de representação de sinais analógicos mediante atrasos de pulsos digitais está sendo muito utilizado como alternativa à representação no domínio da tensão, da corrente ou da carga elétrica. A vantagem desse tipo de representação é que não se vê severamente afetada pela redução da tensão de alimentação dos circuitos integrados fabricados em tecnologias submicrométricas. A taxa de acerto na classificação do sistema desenvolvido é maior que 99% inclusive considerando um offset de até 20mV no comparador de saída. Os circuitos apresentados neste trabalho foram projetados considerando dispositivos da tecnologia TSMC de 90nm. / Neural signals recording through implantable microelectrode arrays in cortex extracellular medium has become an experimental paradigm for neuroscience. Moreover, recent research about motor neuroprostheses has shown that it is possible to decode motor commands from the signals recorded in the cerebral cortex extracellular medium. In both situations, experimental neuroscience and motor neuroprostheses development, one of the issues encountered in the state-of-the-art is the use of integrated circuits (chips) implanted in the brain. In these chips, neural signals measured with microelectrodes are amplified, filtered, processed, and transmitted to an external computer through wires that run through the skull. There is interest in developing implantable chips that transmit signals to the external computer without the need for wires passing through the skull. In the survey of the state-of-the-art it has found the use of such implantable wireless chips in rats and monkeys, but until the date of this writing we have not found reports of application in humans. One of the aspects that must be taken into account in the development of wireless implantable neural interfaces is the bandwidth of the communication channel. The greater the amount of data to be transmitted, the greater the bandwidth required and higher chip heating due to power dissipation. This thesis deals with extracellular neural signals processing systems that aim to reduce the amount of data to be transmitted and in this way to enable wireless transmission. In order to integrate them into an implantable chip, those processing systems must be optimized in terms of area and power consumption. Two processes found in the research of implantable neural interfaces are spike detection and spike sorting. In this thesis solutions for these types of processing are presented considering their implementation by CMOS (Complementary Metal Oxide Semiconductor). For the case of spike detection in this thesis it is presented an alternative for the hardware implementation of a mathematical operator known as NEO (Nonlinear Energy Operator). Through the application of this operator to a neural signal the presence of spikes becomes evident and the noise is attenuated. One of the innovative characteristics of the implementation presented in this thesis is the use of a squarer circuit which consists of only three transistors, as a basic function block for performing operation of NEO. NEO circuit consumes 300 pJ in processing a spike, and was characterized by simulation up to 30 kHz, frequency which is compatible with sampling rates found in the literature. The other processing discussed in this thesis, known as Spike Sorting, is the grouping of electrical impulses recorded by an electrode into categories so that the spikes belonging to the same category were generated by a single neuron. In other words, the goal is to recognize which of the spikes measured by the electrode belong to the same neuron, given that it is possible that several neurons influence the measure performed by a single electrode. A solution for the Spike Sorting suitable in the context of implantable systems, is the template matching. This technique is based on generating templates during an initial phase at the end of which the number of generated templates corresponds to the number of neurons identified by the electrode. In the next phase, the system associates each detected spike to one of the templates generated initially. In this thesis it is proposed a classification systems which performs that second phase of the spike sorting process. This thesis presents the design of a spike classification system based on template matching technique, implemented in CMOS technology. The processing proposed in this work is based on the time-based representation of the analog samples. This kind of representation of analog signals by delays of digital pulses is being widely used as an alternative to the classical representation of samples by voltage, current or electric charge. The advantage of this time-mode representation is that it is not severely affected by reduced supply voltage of integrated circuits manufactured in sub-micrometer technologies. The classification hit rate of the developed system is greater than 99% even when an offset of 20 mV is assumed for the output comparator. All the circuits presented in this work were designed using devices from TSMC 90nm technology.