Algorithmes et processeurs temps réel de traitement de signaux neuronaux pour une plateforme optogénétique sans fil

Tableau d'honneur de la Faculté des études supérieures et postdorales, 2015-2016L’acquisition des signaux électriques provenant des neurones du cerveau permet aux neurobiologistes de mieux comprendre son fonctionnement. Dans le cadre de ce travail, de nouveaux algorithmes de compression des signaux neuronaux sont conçus et présentés. Ces nouveaux algorithmes sont incorporés dans trois nouveaux dispositifs optogénétiques sans fil miniature capable de stimuler optiquement l’activité neuronale et de transmettre sans fil les biopotentiels captés par plusieurs microélectrodes. Deux de ces systèmes sont capables de compresser les signaux provenant de deux microélectrodes ainsi que de stimuler optiquement via deux diodes électroluminescentes (DEL) haute puissance. Afin de réduire la bande passante des transmetteurs sans fil utilisés, ces deux systèmes sont dotés d’un nouvel algorithme de détection des potentiels d’actions qui génère de meilleurs taux de détection que les algorithmes existants, tout en nécessitant moins de ressources matérielles et de temps de processeur. Un troisième dispositif incorporant les algorithmes de détection et de compression fût conçu. Ce dispositif est le seul système optogénétique sans fil comportant 32 canaux de stimulation optique et 32 canaux d’enregistrement électrophysiologiques en parallèle. Il utilise une nouvelle technique de compression par ondelettes permettant d’augmenter significativement le nombre de canaux sous observation sans augmenter la consommation de l’émetteurrécepteur. Cette nouvelle méthode de compression se distingue des méthodes existantes en atteignant de meilleurs taux de compression tout en permettant de reconstruire les signaux compressés avec une meilleure qualité. Au moment de la rédaction de ce mémoire, il s’agit des premiers dispositifs optogénétiques sans fil à offrir simultanément de la stimulation optique multicanal, de l’enregistrement électrophysiologique multicanal ainsi que de la détection/compression in situ des potentiels d’actions. Grâce à leur design novateur et aux innovations apportées par les nouveaux algorithmes de traitement des signaux, les systèmes conçus sont plus légers et plus compacts que les systèmes précédents, rendant ces dispositifs indispensables afin de mener des expériences sur le cerveau de petits animaux libres de leurs mouvements. Les trois systèmes ont été validés avec grand succès par des expériences in vivo sur des souris transgéniques au Centre de Recherche de l’Institut Universitaire en Santé Mentale de Québec (CRIUSMQ).The electrical signals acquisition from the brain’s neurons allows neuroscientists to better understand its functioning. In this work, new neural signals compression algorithms are designed and presented. These new algorithms are incorporated into three new miniature optogenetic wireless devices. These devices are capable to optically stimulate neural activity and to wirelessly transmit the biopotentials captured by several microelectrodes. Two of these systems are able to compress the signals from two microelectrodes and to stimulate optically via two high-power LED. Both systems feature a new spike detection algorithm to reduce the bandwidth used by the wireless transceiver. This new spike detection algorithm differs from existing algorithms by achieving better detection rate while using less material resources and processing time. A third device incorporating the detection and compression algorithms was designed. This device is the only optogenetic wireless system including 32 optical stimulation channels and 32 electrophysiological recording channels in parallel. This new system has the ability to compress the neural signals using a new wavelet compression technique that significantly increase the number of channels under observation without increasing the consumption of the wireless transceiver. In particular, this new compression technique differs from the existing wavelet based compression methods by achieving better compression ratio while allowing to reconstruct the compressed signals with better quality. At the time of writing this thesis, these are the first three devices that offer simultaneous multichannel optical stimulation, multichannel electrophysiological signals recording and on-the-fly spike detection. The resulting systems are more compact and lightweight than previous systems, making these devices essentials to conduct long term experiments on the brains of small freely moving animals. The three systems were validated within in vivo experiments using transgenic mice at the Centre de Recherche de l’Institut Universitaire en Santé Mentale de Québec (CRIUSMQ)

Interfaces neuronales CMOS haute résolution pour l'électrophysiologie et l'optogénétique en boucle fermée

L’avenir de la recherche sur les maladies du cerveau repose sur le développement de nouvelles technologies qui permettront de comprendre comment cet organe si complexe traite, intègre et transfère l’information. Parmi celles-ci, l’optogénétique est une technologie révolutionnaire qui permet d’utiliser de la lumière afin d’activer sélectivement les neurones du cortex d’animaux transgéniques pour observer leur effet dans un vaste réseau biologique. Ce cadre expérimental repose typiquement sur l’observation de l’activité neuronale de souris transgéniques, car elles peuvent exprimer une grande variété de gènes et de maladies et qu’elles sont peu couteuses. Toutefois, la plupart des appareils de mesure ou de stimulation optogénétique disponible ne sont pas appropriés, car ils sont câblés, trop lourds et/ou trop simplistes. Malheureusement, peu de systèmes sans fil existent, et ces derniers sont grandement limités par la bande passante requise pour transmettre les données neuronales, et ils ne fournissent pas de stimulation optogénétique multicanal afin de stimuler et observer plusieurs régions du cerveau. Dans les dispositifs actuels, l’interprétation des données neuronales est effectuée ex situ, alors que la recherche bénéficierait grandement de systèmes sans fil assez intelligents pour interpréter et stimuler les neurones en boucle fermée, in situ. Le but de ce projet de recherche est de concevoir des circuits analogiques-numériques d’acquisition et de traitement des signaux neuronaux, des algorithmes d’analyse et de traitement de ces signaux et des systèmes electro-optiques miniatures et sans fil pour : i) Mener des expériences combinant l’enregistrement neuronal et l’optogénétique multicanal haute résolution avec des animaux libres de leurs mouvements. ii) Mener des expériences optogénétiques synchronisées avec l’observation, c.-à-d. en boucle fermée, chez des animaux libres de leurs mouvements. iii) Réduire la taille, le poids et la consommation énergétique des systèmes optogénétiques sans fil afin de minimiser l’impact de la recherche chez de petits animaux. Ce projet est en 3 phases, et ses principales contributions ont été rapportées dans dix conférences internationales (ISSCC, ISCAS, EMBC, etc.) et quatre articles de journaux publiés ou soumis, ainsi que dans un brevet et deux divulgations. La conception d’un système optogénétique haute résolution pose plusieurs défis importants. Notamment, puisque les signaux neuronaux ont un contenu fréquentiel élevé (_10 kHz), le nombre de canaux sous observation est limité par la bande passante des transmetteurs sans fil (2-4 canaux en général). Ainsi, la première phase du projet a visé le développement d’algorithmes de compression des signaux neuronaux et leur intégration dans un système optogénétique sans fil miniature et léger (2.8 g) haute résolution possédant 32 canaux d’acquisition et 32 canaux de stimulation optique. Le système détecte, compresse et transmet les formes d’onde des potentiels d’action (PA) produits par les neurones avec un field programmable gate array (FPGA) embarqué à faible consommation énergétique. Ce processeur implémente un algorithme de détection des PAs basé sur un seuillage adaptatif, ce qui permet de compresser les signaux en transmettant seulement les formes détectées. Chaque PA est davantage compressé par une transformée en ondelette discrète (DWT) de type Symmlet-2 suivie d’une technique de discrimination et de requantification dynamique des coefficients. Les résultats obtenus démontrent que cet algorithme est plus robuste que les méthodes existantes tout en permettant de reconstruire les signaux compressés avec une meilleure qualité (SNDR moyen de 25 dB _ 5% pour un taux de compression (CR) de 4.2). Avec la détection, des CR supérieurs à 500 sont rapportés lors de la validation in vivo. L’utilisation de composantes commerciales dans des systèmes optogénétiques sans fil augmentela taille et la consommation énergétique, en plus de ne pas être optimisée pour cette application. La seconde phase du projet a permis de concevoir un système sur puce (SoC) complementary metal oxide semiconductor (CMOS) pour faire de l’enregistrement neuronal et de optogénétique multicanal, permettant de réduire significativement la taille et la consommation énergétique comparativement aux alternatives commerciales. Ceci est une contribution importante, car c’est la première puce à être doté de ces deux fonctionnalités. Le SoC possède 10 canaux d’enregistrement et 4 canaux de stimulation optogénétique. La conception du bioamplificateur inclut une bande passante programmable (0.5 Hz - 7 kHz) et un faible bruit referré à l’entré (IRN de 3.2 μVrms), ce qui permet de cibler différents types de signaux biologiques (PA, LFP, etc.). Le convertisseur analogique numérique (ADC) de type Delta- Sigma (DS) MASH 1-1-1 est conçu pour fonctionner de faibles taux de sur-échantillonnage (OSR _50) pour réduire sa consommation et possède une résolution programmable (ENOB de 9.75 Bits avec un OSR de 25). Cet ADC exploite une nouvelle technique réduisant la taille du circuit en soustrayant la sortie de chaque branche du DS dans le domaine numérique, comparativement à la méthode analogique classique. La consommation totale d’un canal d’enregistrement est de 11.2 μW. Le SoC implémente un nouveau circuit de stimulation optique basé sur une source de courant de type cascode avec rétroaction, ce qui permet d’accommoder une large gamme de LED et de tensions de batterie comparativement aux circuits existants. Le SoC est intégré dans un système optogénétique sans fil et validé in vivo. À ce jour et en excluant ce projet, aucun système sans-fil ne fait de l’optogénétique en boucle fermée simultanément au suivi temps réel de l’activité neuronale. Une contribution importante de ce travail est d’avoir développé le premier système optogénétique multicanal qui est capable de fonctionner en boucle fermée et le premier à être validé lors d’expériences in vivo impliquant des animaux libres de leurs mouvements. Pour ce faire, la troisième phase du projet a visé la conception d’un SoC CMOS numérique, appelé neural decoder integrated circuit (ND-IC). Le ND-IC et le SoC développé lors de la phase 2 ont été intégrés dans un système optogénétique sans fil. Le ND-IC possède 3 modules : 1) le détecteur de PA adaptatif, 2) le module de compression possédant un nouvel arbre de tri pour discriminer les coefficients, et 3) le module de classement automatique des PA qui réutilise les données générées par le module de détection et de compression pour réduire sa complexité. Un lien entre un canal d’enregistrement et un canal de stimulation est établi selon l’association de chaque PA à un neurone, grâce à la classification, et selon l’activité de ce neurone dans le temps. Le ND-IC consomme 56.9 μW et occupe 0.08 mm2 par canal. Le système pèse 1.05 g, occupe un volume de 1.12 cm3, possède une autonomie de 3h, et est validé in vivo.The future of brain research lies in the development of new technologies that will help understand how this complex organ processes, integrates and transfers information. Among these, optogenetics is a recent technology that allows the use of light to selectively activate neurons in the cortex of transgenic animals to observe their effect in a large biological network. This experimental setting is typically based on observing the neuronal activity of transgenic mice, as they express a wide variety of genes and diseases, while being inexpensive. However, most available neural recording or optogenetic devices are not suitable, because they are hard-wired, too heavy and/or too simplistic. Unfortunately, few wireless systems exist, and they are greatly limited by the required bandwidth to transmit neural data, while not providing simultaneous multi-channel neural recording and optogenetic, a must for stimulating and observing several areas of the brain. In current devices, the analysis of the neuronal data is performed ex situ, while the research would greatly benefit from wireless systems that are smart enough to interpret and stimulate the neurons in closed-loop, in situ. The goal of this project is to design analog-digital circuits for acquisition and processing of neural signals, algorithms for analysis and processing of these signals and miniature electrooptical wireless systems for: i) Conducting experiments combining high-resolution multi-channel neuronal recording and high-resolution multi-channel optogenetics with freely-moving animals. ii) Conduct optogenetic experiments synchronized with the neural recording, i.e. in closed loop, with freely-moving animals. iii) Increase the resolution while reducing the size, weight and energy consumption of the wireless optogenetic systems to minimize the impact of research with small animals. This project is in 3 phases, and its main contributions have been reported in ten conferences (ISSCC, ISCAS, EMBC, etc.) and four published journal papers, or submitted, as well as in a patent and two disclosures. The design of a high resolution optogenetic system poses several challenges. In particular, since the neuronal signals have a high frequency content (10 kHz), the number of chanv nels under observation is limited by the bandwidth of the wireless transmitters (2-4 channels in general). Thus, the first phase of the project focused on the development of neural signal compression algorithms and their integration into a high-resolution miniature and lightweight wireless optogenetics system (2.8g), having 32 recording channels and 32 optical stimulation channels. This system detects, compresses and transmits the waveforms of the signals produced by the neurons, i.e. action potentials (AP), in real time, via an embedded low-power field programmable gate array (FPGA). This processor implements an AP detector algorithm based on adaptive thresholding, which allows to compress the signals by transmitting only the detected waveforms. Each AP is further compressed by a Symmlet-2 discrete wavelet transform (DWT) followed dynamic discrimination and requantification of the DWT coefficients, making it possible to achieve high compression ratios with a good reconstruction quality. Results demonstrate that this algorithm is more robust than existing approach, while allowing to reconstruct the compressed signals with better quality (average SNDR of 25 dB 5% for a compression ratio (CR) of 4.2). With detection, CRs greater than 500 are reported during the in vivo validation. The use of commercial components in wireless optogenetic systems increases the size and power consumption, while not being optimized for this application. The second phase of the project consisted in designing a complementary metal oxide semiconductor (CMOS) system-on-chip (SoC) for neural recording and multi-channel optogenetics, which significantly reduces the size and energy consumption compared to commercial alternatives. This is important contribution, since it’s the first chip to integrate both features. This SoC has 10 recording channels and 4 optogenetic stimulation channels. The bioamplifier design includes a programmable bandwidth (0.5 Hz -7 kHz) and a low input-referred noise (IRN of 3.2 μVrms), which allows targeting different biological signals (AP, LFP, etc.). The Delta-Sigma (DS) MASH 1-1-1 low-power analog-to-digital converter (ADC) is designed to work with low OSR (50), as to reduce its power consumption, and has a programmable resolution (ENOB of 9.75 bits with an OSR of 25). This ADC uses a new technique to reduce its circuit size by subtracting the output of each DS branch in the digital domain, rather than in the analog domain, as done conventionally. A recording channel, including the bioamplifier, the DS and the decimation filter, consumes 11.2 μW. Optical stimulation is performed with an on-chip LED driver using a regulated cascode current source with feedback, which accommodates a wide range of LED parameters and battery voltages. The SoC is integrated into a wireless optogenetic platform and validated in vivo.To date and excluding this project, no wireless system is making closed-loop optogenetics simultaneously to real-time monitoring of neuronal activity. An important contribution of this work is to have developed the first multi-channel optogenetic system that is able to work in closed-loop, and the first to be validated during in vivo experiments involving freely-moving animals. To do so, the third phase of the project aimed to design a digital CMOS chip, called neural decoder integrated circuit (ND-IC). The ND-IC and the SoC developed in Phase 2 are integrated within a wireless optogenetic system. The ND-IC has 3 main cores: 1) the adaptive AP detector core, 2) the compression core with a new sorting tree for discriminating the DWT coefficients, and 3 ) the AP automatic classification core that reuses the data generated by the detection and compression cores to reduce its complexity. A link between a recording channel and a stimulation channel is established according to the association of each AP with a neuron, thanks to the classification, and according to the bursting activity of this neuron. The ND-IC consumes 56.9 μW and occupies 0.08 mm2 per channel. The system weighs 1.05 g, occupies a volume of 1.12 cm3, has an autonomy of 3h, and is validated in vivo

A low-cost, wireless, 3-D-printed custom armband for sEMG hand gesture recognition

Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost ( 150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly (p < 0.05) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository

A multichannel wireless sEMG sensor endowing a 0.13 μm CMOS mixed-signal SoC

This paper presents a wireless multichannel surface electromyography (sEMG) sensor which features a custom 0.13μm CMOS mixed-signal system-on-chip (SoC) analog frontend circuit. The proposed sensor includes 10 sEMG recording channels with tunable bandwidth (BW) and analog-to-digital converter (ADC) resolution. The SoC includes 10x bioamplifiers, 10x 3 rd order ΔΣ MASH 1-1-1 ADC, and 10x on-chip decimation filters (DF). This SoC provides the sEMG samples data through a serial peripheral interface (SPI) bus to a microcontroller unit (MCU) that then transfers the data to a wireless transceiver. We report sEMG waveforms acquired using a custom multichannel electrode module, and a comparison with a commercial grade system. Results show that the proposed integrated wireless SoC-based system compares well with the commercial grade sEMG recording system. The sensor has an input-referred noise of 2.5 μVrms (BW of 10-500 Hz), an input-dynamic range of 6 mVpp, a programmable sampling rate of 2 ksps, for sEMG, while consuming only 7.1 μW/Ch for the SoC (w/ ADC & DF) and 21.8 mW of power for the sensor (Transceiver, MCU, etc.). The system lies on a 1.5 × 2.0 cm 2 printed circuit board and weights <; 1 g

A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between offline and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p<0.05) outperforms using fine-tuning as the recalibration technique.Comment: 10 Pages. The last three authors shared senior authorshi

A wireless electro-optic platform for multimodal electrophysiology and optogenetics in freely moving rodents

This paper presents the design and the utilization of a wireless electro-optic platform to perform simultaneous multimodal electrophysiological recordings and optogenetic stimulation in freely moving rodents. The developed system can capture neural action potentials (AP), local field potentials (LFP) and electromyography (EMG) signals with up to 32 channels in parallel while providing four optical stimulation channels. The platform is using commercial off-the-shelf components (COTS) and a low-power digital field-programmable gate array (FPGA), to perform digital signal processing to digitally separate in real time the AP, LFP and EMG while performing signal detection and compression for mitigating wireless bandwidth and power consumption limitations. The different signal modalities collected on the 32 channels are time-multiplexed into a single data stream to decrease power consumption and optimize resource utilization. The data reduction strategy is based on signal processing and real-time data compression. Digital filtering, signal detection, and wavelet data compression are used inside the platform to separate the different electrophysiological signal modalities, namely the local field potentials (1–500 Hz), EMG (30–500 Hz), and the action potentials (300–5,000 Hz) and perform data reduction before transmitting the data. The platform achieves a measured data reduction ratio of 7.77 (for a firing rate of 50 AP/second) and weights 4.7 g with a 100-mAh battery, an on/off switch and a protective plastic enclosure. To validate the performance of the platform, we measured distinct electrophysiology signals and performed optogenetics stimulation in vivo in freely moving rondents. We recorded AP and LFP signals with the platform using a 16-microelectrode array implanted in the primary motor cortex of a Long Evans rat, both in anesthetized and freely moving conditions. EMG responses to optogenetic Channelrhodopsin-2 induced activation of motor cortex via optical fiber were also recorded in freely moving rodents

A transferable adaptive domain adversarial neural network for virtual reality augmented EMG-Based gesture recognition

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the off line accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between off line and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different-recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p <; 0.05) outperforms using fine-tuning as the recalibration technique

A wireless sEMG-based body-machine interface for assistive technology devices

Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO

Smart autonomous electro-optic platforms enabling innovative brain therapies

The future of brain research lies in the application of new technologies drawing from the latest developments in biology, physics and engineering to advance our understanding of how this complex organ processes, integrates and transfers information. Among these, optogenetics is a groundbreaking technology that allows using light to selectively activate neurons in the cortex of transgenic animals, usually mice, to observe its effect in large biological networks. A new research paradigm drawing from these advances consists of synchronizing optogenetic stimulation with electrophysiology recordings, to close the loop and to regulate the neural microcircuits, or to repair them. Such an approach holds promise to accelerate the development of new therapeutics against brain diseases by enabling entirely new experimental research scenarios with freely behaving animal models. As a result, the development of advanced wireless microelectronic implantable systems to elicit, ex tract and process brain data in real time has become a source of significant interest. This paper reviews the design challenges and the state-of-the- art technology in this field. We present the design of a complete electro-optic device for preforming optogenetics and multichannel electrophysiology in a closed-loop (CL) system with live neurons. We cover the design of the different CMOS integrated building blocks involved in this system to perform photostimulation and multichannel neural recording in parallel. We describe advanced hardware strategies to perform action potential (AP) detection, neural data compression and AP sorting in real-time, over several parallel recording channels for enabling real-time CL neural control. Finally, we present CL experimental results obtained in vivo with an electro-optic prototype