Development of an analysis pipeline for human microelectrode recordings in Parkinson’s disease

Thesis to obtain the master degree in Biomedical EngineeringBackground: Deep brain stimulation is a common treatment for advanced Parkinson’s Disease (PD). Intraoperative microelectrode recordings (MER) along preplanned trajectories are often used for accurate identification of subthalamic nucleus (STN), a common target for deep brain stimulation (DBS) in PD. However, this identification is performed manually and can be difficult in regions of transition. Misidentification may lead to suboptimal location of the DBS lead and inadequate clinical outcomes. Methods: A tool for unsupervised analysis and spike-sorting of human MER signals with feature extraction was developed. We also trained and tested a hybrid unsupervised/supervised machine learning approach that uses extracted MER time, frequency and noise properties for high-accuracy identification of STN. Lastly, we compared neurophysiological characteristics of different STN functional segments. Results: We obtained a classification accuracy of 96:28 3:15 % (30 trajectories, 5 patients) for individual STN-DBS surgery MER using an approach of "leave one subject out" validation with support vector machine classifier, all features based on time and frequency domain and human expert labels. The unsupervised sorting approach allowed us to sort a total of 357 STN neurons in 5 subjects. Dividing the STN in a dorsal, probably motor region, and a ventral, probably non-motor portion, we’ve found a higher burst rate (median (interquartile range) of 1.8 (1.5) vs 1.15 (0.05) bursts/s, p=0.001) and firing rate (median (interquartile range) of 21.4 (16.85) vs. 15.3 (14.33), p=0.013) of dorsal STN neurons among other features. Ongoing work will refine these results using anatomical gold standard through lead trajectory reconstruction, fused with an STN functional subdivision atlas. Conclusions: We’ve developed a tool for human MER analysis and extraction of related features, that provided good preliminary results in STN classification. In line with the literature, we were able to find preliminary activity differences in functionally segregated STN segments. This tool is fast and generalizable for other brain regions. Ongoing work using patient’s anatomy can further validate its’ usefulness in optimizing electrode placement and research purposes.A Estimulação Cerebral Profunda é um técnica utilizada para o tratamento dos sintomas motores da Doença de Parkinson com recurso a estimulação eléctrica intracraniana. Um dos alvos mais frequentemente utilizados para o tratamento é o núcleo subtalâmico e uma colocação precisa dos eléctrodos na região correcta é fulcral para o sucesso terapêutico e minimização de efeitos secundários. Esta cirurgia faz-se com recurso a estereotaxia e monitorização intra-operatória. Previamente à cirurgia, as coordenadas do alvo são estabelecidas e é definido o núcleo subtalâmico utilizando imagens pré-operatórias de ressonância magnética e de tomografia computadorizada do doente. Intra-operatoriamente são utilizados registos de microeléctrodos a diferentes profundidades em trajetórias pré-definidas para verificar as coordenadas estabelecidas e identificar -pela actividade eléctrica- a localização dos eléctrodos no cérebro. No entanto, a identificação do núcleo subtalâmico com registos de microeléctrodos é frequentemente realizada por inspeção visual, e pode ser difícil em regiões de transição. Erros na identificação podem levar a um posicionamento subóptimo dos elétrodos e a um mau resultado clínico. O correcto posicionamento do eléctrodo final de estimulação na porção sensoriomotora do núcleo subtalâmico está relacionado com os melhores resultados clínicos de acordo com a literatura. No entanto não é possível identificar claramente esta subdivisão com base a inspecção visual dos registos. O objetivo do presente estudo é o desenvolvimento de ferramentas para análise de registos de microeléctrodos em diferentes áreas do cérebro e identificação do núcleo subtalâmico, e da porção sensoriomotora, em doentes com Doença de Parkinson submetidos a estimulação cerebral profunda. Métodos Foram desenvolvidas ferramentas para análise do sinal, spike sorting e extração de características por processamento não supervisionado de registos de microeléctrodos humanos. As características relativas ao domínio do tempo e frequência foram obtidas para cada sinal através de medidas estatísticas (média, mediana e desvio padrão) dos registos fracionados em segmentos sobrepostos, para minimizar os efeitos do ruído. As características relacionadas com a atividade neuronal foram também extraídas após deteção e identificação dos diferentes neurónios em cada registo de forma não supervisionada. A localização dos diferentes registos ao longo do trajecto foi realizada por um especialista através de inspeção visual aleatória de todos os sinais. As características extraídas nas regiões do núcleo subtalâmico foram comparadas com as registadas fora de esta área. Posteriormente, uma abordagem híbrida de classificação utilizando métodos de aprendizagem automática -machine learning- foi treinada e testada para identificação dos registos localizados no núcleo subtalâmico utilizando as características extraídas baseadas no domínio do tempo e frequência. Para comparar as sub-regiões funcionais do núcleo subtalâmico, utilizaram-se os sinais identificados em esta região e dividiram-se estas profundidades etiquetadas pelo especialista como núcleo subtalâmico na porção mais dorsal (que terá maior probabilidade de ser motora) e outra ventral (que terá maior probabilidade de ser não motora). Utilizando esta subdivisão, compararamse as características neurofisiológicas relativas às diferentes sub-regiões funcionais com testes estatísticos. Conhecendo as limitações da classificação por inspecção visual, desenvolveu-se uma metodologia para refinamento das localizações dos diferentes registos baseada na reconstrução da trajetória dos eléctrodos implantados, utilizando imagens pre- e pos- operatórias. Esta reconstrução foi sobreposta a um atlas contendo as subdivisões funcionais do núcleo subtalâmico. Os resultados preliminares desta aplicação num sujeito, permitiram mostrar uma classificação mais realista destas sub-regiões na identificação da área motora vs. não motora (límbica e associativa). Resultados Foram identificadas várias características significativamente diferentes no domínio do tempo e frequência, entre os sinais classificados como pertencentes ao Núcleo Subtalámico e os sinais não-pertencentes. Estas diferenças permitiram o desenvolvimento de classificadores do tipo de máquinas de vectores de suporte, utilizando as localizações dos diferentes registos tendo como base a classificação baseada em inspecção visual por um especialista. Utilizando os sinais de maior certeza de classificação, obteve-se uma precisão de classificação do núcleo subtalâmico de 96,18 3,15 % (para 30 trajetórias, 5 pacientes) usando uma abordagem de validação cruzada de “deixar um sujeito fora"com máquina de vectores de suporte linear. Este modelo de validação garante que os dados relativos aos doentes utilizados para treinar o modelo não foram usados posteriormente para testar o mesmo. Utilizando todos os sinais considerados como núcleo subtalâmico independentemente do nível de certeza, obteve-se uma classificação de 84,32 5,75 %. A ferramenta não supervisionada para spike sorting permitiu a identificação de um total de 357 neurónios do núcleo subtalâmico identificado com a máxima certeza em 5 doentes (155 registos de microeléctrodos de 10 segundos de duração) e 521 neurónios foram extraídos de 420 sinais localizados fora do núcleo subtalâmico. Utilizando segmentos de 10 segundos livres de artefactos, identificamos 258 neurónios em 116 sinais do núcleo subtalâmico e 329 neurónios em 228 registos de não-Subtalámico. Foram encontradas diferenças significativas em em 13 das 15 características analisadas comparando STN com não-STN. As subregiões funcionais do núcleo subtalâmico (dorsal vs. ventral) foram comparadas quanto às caracteristicas de tempo, frequência e actividade neuronal. Encontraram-se diferenças significativas em 6 características extraídas dos sinais sem artefactos, consistentes com a literatura e também em uma característica de frequência. Em linha com o descrito previamente, encontramos um burts rate mais elevado na subdivisão dorsal em relação à ventral (mediana (amplitude interquartil) of 1.8(1.5) vs 1.15(0.05) bursts/s, p=0.001) e um firing rate mais alto (mediana (amplitude interquartil) of 21.4(16.85) vs. 15.3(14.33), p=0.013) entre outras características. Os resultados preliminares da análise de sinal refinado por imagem num sujeito, mostraram uma alta accuracy do expert na classificação de sinais STN/não-STN comparados com imagem (>85%). Apesar de neste sujeito não se obterem diferenças significativas entre regiões nas características extraídas, esta analise mostrou que divisão heurística dorsal/ventral era insuficiente para proceder à analise apropriada. Conclusões Foi desenvolvida uma ferramenta para análise de registo de microeléctrodos em humanos e para extração de características no domínio do tempo e frequência, que forneceu bons resultados na classificação do núcleo subtalâmico utilizando a identificação da localização de cada registo por um especialista. A análise e extração de características relativas à atividade neuronal foi realizada por uma ferramenta não supervisionada, que foi desenvolvida combinando algoritmos existentes e que pode ser generalizável a registos em outras regiões ou outro tipo de registos. Esta ferramenta permitiu a identificação de características que permitem diferenciar os sinais colhidos no núcleo subtalâmico dos identificados fora de esta região. De acordo com a literatura, fomos capazes de identificar diferenças em segmentos funcionalmente segregados do núcleo subtalâmico. O trabalho em curso, com refinação anatómica, vai-nos permitir avaliar a sua utilidade em optimizar o posicionamento dos elétrodos, podendo também ser utilizado para fins de investigação.N/

On the use of wavelet denoising and spike sorting techniques to process electroneurographic signals recorded using intraneural electrodes.

Among the possible interfaces with the peripheral nervous system (PNS), intraneural electrodes represent an interesting solution for their potential advantages such as the possibility of extracting spikes from electroneurographic (ENG) signals. Their use could increase the precision and the amount of information which can be detected with respect to other processing methods. In this study, in order to verify this assumption, thin-film longitudinal intrafascicular electrodes (tfLIFE) were implanted in the sciatic nerve of rabbits. Various sensory stimuli were applied to the hind limb of the animal and the elicited ENG signals were recorded using the tfLIFEs. These signals were processed to determine whether the different types of information can be decoded. Signals were wavelet denoised and spike sorted. Support vector machines were trained to use the spike waveforms found to infer the stimulus applied to the rabbit. This approach was also compared with previously used ENG-processing methods. The results indicate that the combination of wavelet denoising and spike sorting techniques can increase the amount of information extractable from ENG signals recorded with intraneural electrodes. This strategy could allow the development of more effective closed-loop neuroprostheses and hybrid bionic systems connecting the human nervous system with artificial devices

Processing and analysis of multichannel extracellular neuronal signals: state-of-the-art and challenges

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data

Algorithms for Neural Prosthetic Applications

abstract: In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods.Dissertation/ThesisDoctoral Dissertation Bioengineering 201

Acquisition systems and decoding algorithms of peripheral neural signals for prosthetic applications

During the years, neuroprosthetic applications have obtained a great deal of attention by the international research, especially in the bioengineering field, thanks to the huge investments on several proposed projects funded by the political institutions which consider the treatment of this particular disease of fundamental importance for the global community. The aim of these projects is to find a possible solution to restore the functionalities lost by a patient subjected to an upper limb amputation trying to develop, according to physiological considerations, a communication link between the brain in which the significant signals are generated and a motor prosthesis device able to perform the desired action. Moreover, the designed system must be able to give back to the brain a sensory feedback about the surrounding world in terms of pressure or temperature acquired by tactile biosensors placed at the surface of the cybernetic hand. It in fact allows to execute involuntarymovements when for example the armcomes in contact with hot objects. The development of such a closed-loop architecture involves the need to address some critical issues which depend on the chosen approach. Several solutions have been proposed by the researches of the field, each one differing with respect to where the neural signals are acquired, either at the central nervous systemor at the peripheral one,most of themfollowing the former even that the latter is always considered by the amputees amore natural way to handle the artificial limb. This research work is based on the use of intrafascicular electrodes directly implanted in the residual peripheral nerves of the stump which represents a good compromise choice in terms of invasiveness and selectivity extracting electroneurographic (ENG) signals from which it is possible to identify the significant activity of a quite limited number of neuronal cells. In the perspective of the hardware implementation of the resulting solution which can work autonomously without any intervention by the amputee in an adaptive way according to the current characteristics of the processed signal and by using batteries as power source allowing portability, it is necessary to fulfill the tight constraints imposed by the application under consideration involved in each of the various phases which compose the considered closed-loop system. Regarding to the recording phase, the implementation must be able to remove the unwanted interferences mainly due to the electro-stimulations of themuscles placed near the electrodes featured by an order of magnitude much greater in comparison to that of the signals of interest amplifying the frequency components belonging to the significant bandwidth, and to convert them with a high resolution in order to obtain good performance at the next processing phases. To this aim, a recording module for peripheral neural signals will be presented, based on the use of a sigma-delta architecture which is composed by two main parts: an analog front-end stage for neural signal acquisition, pre-filtering and sigma-delta modulation and a digital unit for sigma-delta decimation and system configuration. Hardware/software cosimulations exploiting the Xilinx System Generator tool in Matlab Simulink environment and then transistor-level simulations confirmed that the system is capable of recording neural signals in the order of magnitude of tens of μV rejecting the huge low-frequency noise due to electromyographic interferences. The same architecture has been then exploited to implement a prototype of an 8-channel implantable electronic bi-directional interface between the peripheral nervous system and the neuro-controlled hand prosthesis. The solution includes a custom designed Integrated Circuit (0.35μm CMOS technology), responsible of the signal pre-filtering and sigma-delta modulation for each channel and the neural stimuli generation (in the opposite path) based on the directives sent by a digital control systemmapped on a low-cost Xilinx FPGA Spartan-3E 1600 development board which also involves the multi-channel sigma-delta decimation with a high-order band-pass filter as first stage in order to totally remove the unwanted interferences. In this way, the analog chip can be implanted near the electrodes thanks to its limited size avoiding to add a huge noise to theweak neural signals due to longwires connections and to cause heat-related infections, shifting the complexity to the digital part which can be hosted on a separated device in the stump of the amputeewithout using complex laboratory instrumentations. The system has been successfully tested from the electrical point of view and with in-vivo experiments exposing good results in terms of output resolution and noise rejection even in case of critical conditions. The various output channels at the Nyquist sampling frequency coming from the acquisition system must be processed in order to decode the intentions of movements of the amputee, applying the correspondent electro-mechanical stimulation in input to the cybernetic hand in order to perform the desired motor action. Different decoding approaches have been presented in the past, the majority of them were conceived starting from the relative implementation and performance evaluation of their off-line version. At the end of the research, it is necessary to develop these solutions on embedded systems performing an online processing of the peripheral neural signals. However, it is often possible only by using complex hardware platforms clocked at very high operating frequencies which are not be compliant with the low-power requirements needed to allow portability for the prosthetic device. At present, in fact, the important aspect of the real-time implementation of sophisticated signal processing algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited resources of the former may have on the efficiency/effectiveness of any given algorithm. In this research work it has been addressed the optimization of a state-of-the-art algorithmfor PNS signals decoding that is a step forward for its real-time, full implementation onto a floating-point Digital Signal Processor (DSP). Beyond low-level optimizations, different solutions have been proposed at an high level in order to find the best trade-off in terms of effectiveness/efficiency. A latency model, obtained through cycle accurate profiling of the different code sections, has been drawn in order to perform a fair performance assessment. The proposed optimized real-time algorithmachieves up to 96% of correct classification on real PNS signals acquired through tf-LIFE electrodes on animals, and performs as the best off-line algorithmfor spike clustering on a synthetic cortical dataset characterized by a reasonable dissimilarity between the spikemorphologies of different neurons. When the real-time requirements are joined to the fulfilment of area and power minimization for implantable/portable applications, such as for the target neuroprosthetic devices, only custom VLSI implementations can be adopted. In this case, every part of the algorithmshould be carefully tuned. To this aim, the first preprocessing stage of the decoding algorithmbased on the use of aWavelet Denoising solution able to remove also the in-band noise sources has been deeply analysed in order to obtain an optimal hardware implementation. In particular, the usually overlooked part related to threshold estimation has been evaluated in terms of required hardware resources and functionality, exploiting the commercial Xilinx System Generator tool for the design of the architecture and the co-simulation. The analysis has revealed how the widely used Median Absolute Deviation (MAD) could lead o hardware implementations highly inefficient compared to other dispersion estimators demonstrating better scalability, relatively to the specific application. Finally, two different hardware implementations of the reference decoding algorithm have been presented highlighting pros and cons of each one of them. Firstly, a novel approach based on high-level dataflow description and automatic hardware generation is presented and evaluated on the on-line template-matching spike sorting algorithmwhich represents the most complex processing stage. It starts from the identification of the single kernels with the greater computational complexity and using their dataflow description to generate the HDL implementation of a coarse-grained reconfigurable global kernel characterized by theminimumresources in order to reduce the area and the energy dissipation for the fulfilment of the low-power requirements imposed by the application. Results in the best case have revealed a 71%of area saving compared tomore traditional solutions,without any accuracy penalty. With respect to single kernels execution, better latency performance are achievable stillminimizing the number of adopted resources. The performance in terms of latency can also be improved by tuning the implemented parallelismin the light of a defined number of channels and real-time constraints, by using more than one reconfigurable global kernel in order that they can be exploited to perform the same or different kernels at the same time in a parallel way, due to the fact that each one can execute the relative processing only in a sequential way. For this reason, a second FPGA-based prototype has been proposed based on the use of aMulti-Processor System-on-Chip (MPSoC) embedded architecture. This prototype is capable of respecting the real-time constraints posed by the application when clocked at less than 50 MHz, in comparison to 300 MHz of the previous DSP implementation. Considering that the application workload is extremely data dependent and unpredictable due to the sparsity of the neural signals, the architecture has to be dimensioned taking into account critical worst-case operating conditions in order to always ensure the correct functionality. To compensate the resulting overprovisioning of the system architecture, a software-controllable power management based on the use of clock gating techniques has been integrated in order tominimize the dynamic power consumption of the resulting solution. Summarizing, this research work can be considered a sort of proof-of-concept for the proposed techniques considering all the design issues which characterize each stage of the closed-loop system in the perspective of a portable low-power real-time hardware implementation of the neuro-controlled prosthetic device

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript

Acquisition systems and decoding algorithms of peripheral neural signals for prosthetic applications

During the years, neuroprosthetic applications have obtained a great deal of attention by the international research, especially in the bioengineering field, thanks to the huge investments on several proposed projects funded by the political institutions which consider the treatment of this particular disease of fundamental importance for the global community. The aim of these projects is to find a possible solution to restore the functionalities lost by a patient subjected to an upper limb amputation trying to develop, according to physiological considerations, a communication link between the brain in which the significant signals are generated and a motor prosthesis device able to perform the desired action. Moreover, the designed system must be able to give back to the brain a sensory feedback about the surrounding world in terms of pressure or temperature acquired by tactile biosensors placed at the surface of the cybernetic hand. It in fact allows to execute involuntarymovements when for example the armcomes in contact with hot objects. The development of such a closed-loop architecture involves the need to address some critical issues which depend on the chosen approach. Several solutions have been proposed by the researches of the field, each one differing with respect to where the neural signals are acquired, either at the central nervous systemor at the peripheral one,most of themfollowing the former even that the latter is always considered by the amputees amore natural way to handle the artificial limb. This research work is based on the use of intrafascicular electrodes directly implanted in the residual peripheral nerves of the stump which represents a good compromise choice in terms of invasiveness and selectivity extracting electroneurographic (ENG) signals from which it is possible to identify the significant activity of a quite limited number of neuronal cells. In the perspective of the hardware implementation of the resulting solution which can work autonomously without any intervention by the amputee in an adaptive way according to the current characteristics of the processed signal and by using batteries as power source allowing portability, it is necessary to fulfill the tight constraints imposed by the application under consideration involved in each of the various phases which compose the considered closed-loop system. Regarding to the recording phase, the implementation must be able to remove the unwanted interferences mainly due to the electro-stimulations of themuscles placed near the electrodes featured by an order of magnitude much greater in comparison to that of the signals of interest amplifying the frequency components belonging to the significant bandwidth, and to convert them with a high resolution in order to obtain good performance at the next processing phases. To this aim, a recording module for peripheral neural signals will be presented, based on the use of a sigma-delta architecture which is composed by two main parts: an analog front-end stage for neural signal acquisition, pre-filtering and sigma-delta modulation and a digital unit for sigma-delta decimation and system configuration. Hardware/software cosimulations exploiting the Xilinx System Generator tool in Matlab Simulink environment and then transistor-level simulations confirmed that the system is capable of recording neural signals in the order of magnitude of tens of μV rejecting the huge low-frequency noise due to electromyographic interferences. The same architecture has been then exploited to implement a prototype of an 8-channel implantable electronic bi-directional interface between the peripheral nervous system and the neuro-controlled hand prosthesis. The solution includes a custom designed Integrated Circuit (0.35μm CMOS technology), responsible of the signal pre-filtering and sigma-delta modulation for each channel and the neural stimuli generation (in the opposite path) based on the directives sent by a digital control systemmapped on a low-cost Xilinx FPGA Spartan-3E 1600 development board which also involves the multi-channel sigma-delta decimation with a high-order band-pass filter as first stage in order to totally remove the unwanted interferences. In this way, the analog chip can be implanted near the electrodes thanks to its limited size avoiding to add a huge noise to theweak neural signals due to longwires connections and to cause heat-related infections, shifting the complexity to the digital part which can be hosted on a separated device in the stump of the amputeewithout using complex laboratory instrumentations. The system has been successfully tested from the electrical point of view and with in-vivo experiments exposing good results in terms of output resolution and noise rejection even in case of critical conditions. The various output channels at the Nyquist sampling frequency coming from the acquisition system must be processed in order to decode the intentions of movements of the amputee, applying the correspondent electro-mechanical stimulation in input to the cybernetic hand in order to perform the desired motor action. Different decoding approaches have been presented in the past, the majority of them were conceived starting from the relative implementation and performance evaluation of their off-line version. At the end of the research, it is necessary to develop these solutions on embedded systems performing an online processing of the peripheral neural signals. However, it is often possible only by using complex hardware platforms clocked at very high operating frequencies which are not be compliant with the low-power requirements needed to allow portability for the prosthetic device. At present, in fact, the important aspect of the real-time implementation of sophisticated signal processing algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited resources of the former may have on the efficiency/effectiveness of any given algorithm. In this research work it has been addressed the optimization of a state-of-the-art algorithmfor PNS signals decoding that is a step forward for its real-time, full implementation onto a floating-point Digital Signal Processor (DSP). Beyond low-level optimizations, different solutions have been proposed at an high level in order to find the best trade-off in terms of effectiveness/efficiency. A latency model, obtained through cycle accurate profiling of the different code sections, has been drawn in order to perform a fair performance assessment. The proposed optimized real-time algorithmachieves up to 96% of correct classification on real PNS signals acquired through tf-LIFE electrodes on animals, and performs as the best off-line algorithmfor spike clustering on a synthetic cortical dataset characterized by a reasonable dissimilarity between the spikemorphologies of different neurons. When the real-time requirements are joined to the fulfilment of area and power minimization for implantable/portable applications, such as for the target neuroprosthetic devices, only custom VLSI implementations can be adopted. In this case, every part of the algorithmshould be carefully tuned. To this aim, the first preprocessing stage of the decoding algorithmbased on the use of aWavelet Denoising solution able to remove also the in-band noise sources has been deeply analysed in order to obtain an optimal hardware implementation. In particular, the usually overlooked part related to threshold estimation has been evaluated in terms of required hardware resources and functionality, exploiting the commercial Xilinx System Generator tool for the design of the architecture and the co-simulation. The analysis has revealed how the widely used Median Absolute Deviation (MAD) could lead o hardware implementations highly inefficient compared to other dispersion estimators demonstrating better scalability, relatively to the specific application. Finally, two different hardware implementations of the reference decoding algorithm have been presented highlighting pros and cons of each one of them. Firstly, a novel approach based on high-level dataflow description and automatic hardware generation is presented and evaluated on the on-line template-matching spike sorting algorithmwhich represents the most complex processing stage. It starts from the identification of the single kernels with the greater computational complexity and using their dataflow description to generate the HDL implementation of a coarse-grained reconfigurable global kernel characterized by theminimumresources in order to reduce the area and the energy dissipation for the fulfilment of the low-power requirements imposed by the application. Results in the best case have revealed a 71%of area saving compared tomore traditional solutions,without any accuracy penalty. With respect to single kernels execution, better latency performance are achievable stillminimizing the number of adopted resources. The performance in terms of latency can also be improved by tuning the implemented parallelismin the light of a defined number of channels and real-time constraints, by using more than one reconfigurable global kernel in order that they can be exploited to perform the same or different kernels at the same time in a parallel way, due to the fact that each one can execute the relative processing only in a sequential way. For this reason, a second FPGA-based prototype has been proposed based on the use of aMulti-Processor System-on-Chip (MPSoC) embedded architecture. This prototype is capable of respecting the real-time constraints posed by the application when clocked at less than 50 MHz, in comparison to 300 MHz of the previous DSP implementation. Considering that the application workload is extremely data dependent and unpredictable due to the sparsity of the neural signals, the architecture has to be dimensioned taking into account critical worst-case operating conditions in order to always ensure the correct functionality. To compensate the resulting overprovisioning of the system architecture, a software-controllable power management based on the use of clock gating techniques has been integrated in order tominimize the dynamic power consumption of the resulting solution. Summarizing, this research work can be considered a sort of proof-of-concept for the proposed techniques considering all the design issues which characterize each stage of the closed-loop system in the perspective of a portable low-power real-time hardware implementation of the neuro-controlled prosthetic device