Resource efficient on-node spike sorting

Current implantable brain-machine interfaces are recording multi-neuron activity by utilising multi-channel, multi-electrode micro-electrodes. With the rapid increase in recording capability has come more stringent constraints on implantable system power consumption and size. This is even more so with the increasing demand for wireless systems to increase the number of channels being monitored whilst overcoming the communication bottleneck (in transmitting raw data) via transcutaneous bio-telemetries. For systems observing unit activity, real-time spike sorting within an implantable device offers a unique solution to this problem. However, achieving such data compression prior to transmission via an on-node spike sorting system has several challenges. The inherent complexity of the spike sorting problem arising from various factors (such as signal variability, local field potentials, background and multi-unit activity) have required computationally intensive algorithms (e.g. PCA, wavelet transform, superparamagnetic clustering). Hence spike sorting systems have traditionally been implemented off-line, usually run on work-stations. Owing to their complexity and not-so-well scalability, these algorithms cannot be simply transformed into a resource efficient hardware. On the contrary, although there have been several attempts in implantable hardware, an implementation to match comparable accuracy to off-line within the required power and area requirements for future BMIs have yet to be proposed. Within this context, this research aims to fill in the gaps in the design towards a resource efficient implantable real-time spike sorter which achieves performance comparable to off-line methods. The research covered in this thesis target: 1) Identifying and quantifying the trade-offs on subsequent signal processing performance and hardware resource utilisation of the parameters associated with analogue-front-end. Following the development of a behavioural model of the analogue-front-end and an optimisation tool, the sensitivity of the spike sorting accuracy to different front-end parameters are quantified. 2) Identifying and quantifying the trade-offs associated with a two-stage hybrid solution to realising real-time on-node spike sorting. Initial part of the work focuses from the perspective of template matching only, while the second part of the work considers these parameters from the point of whole system including detection, sorting, and off-line training (template building). A set of minimum requirements are established which ensure robust, accurate and resource efficient operation. 3) Developing new feature extraction and spike sorting algorithms towards highly scalable systems. Based on waveform dynamics of the observed action potentials, a derivative based feature extraction and a spike sorting algorithm are proposed. These are compared with most commonly used methods of spike sorting under varying noise levels using realistic datasets to confirm their merits. The latter is implemented and demonstrated in real-time through an MCU based platform.Open Acces

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

An optogenetic headstage for optical stimulation and neural recording in life science applications

L'optogénétique est une nouvelle méthode de contrôle de l’activité neuronale dans laquelle la lumière est employée pour activer ou arrêter certains neurones. Dans le cadre de ce travail, un dispositif permettant l’acquisition de signaux neuronaux et conduisant à une stimulation optogénétique de façon multicanale et temps-réel a été conçu. Cet outil est muni de deux canaux de stimulation optogénétique et de deux canaux de lecture des signaux neuronaux. La source de lumière est une DEL qui peut consommer jusqu’à 150 milliampères. Les signaux neuronaux acquis sont transmis à un ordinateur par une radio. Les dimensions sont d’environ 20×20×15 mm3 et le poids est de moins de 7 grammes, rendant l’appareil utile pour les expériences sur les petits animaux libres. Selon nos connaissances actuelles, le résultat de ce projet constitue le premier appareil de recherche optogénétique sans-fil, compact offrant la capture de signaux cérébraux et la stimulation optique simultanée.Optogenetics is a new method for controlling the neural activity where light is used to activate or silence, with high spatial and temporal resolution, genetically light-sensitized neurons. In optogenetics, a light source such as a LED, targets light-sensitized neurons. In this work, a light-weight wireless animal optogenetic headstage has been designed that allows multi-channel simultaneous real-time optical stimulation and neural recording. This system has two optogenetic stimulation channels and two electrophysiological reading channels. The optogenetic stimulation channels benefit from high-power LEDs (sinking 150 milliamps) with flexible stimulation patterns and the recorded neural data is wirelessly sent to a computer. The dimensions of the headstage are almost 20×20×15 mm3 and it weighs less than 7 grams. This headstage is suitable for tests on small freely-moving rodents. To the best of our knowledge, this is the first reported fully wireless headstage to offer simultaneous multichannel optical stimulation along with multichannel neural recording capability

Real-time neural signal processing and low-power hardware co-design for wireless implantable brain machine interfaces

Intracortical Brain-Machine Interfaces (iBMIs) have advanced significantly over the past two decades, demonstrating their utility in various aspects, including neuroprosthetic control and communication. To increase the information transfer rate and improve the devices’ robustness and longevity, iBMI technology aims to increase channel counts to access more neural data while reducing invasiveness through miniaturisation and avoiding percutaneous connectors (wired implants). However, as the number of channels increases, the raw data bandwidth required for wireless transmission also increases becoming prohibitive, requiring efficient on-implant processing to reduce the amount of data through data compression or feature extraction. The fundamental aim of this research is to develop methods for high-performance neural spike processing co-designed within low-power hardware that is scaleable for real-time wireless BMI applications. The specific original contributions include the following: Firstly, a new method has been developed for hardware-efficient spike detection, which achieves state-of-the-art spike detection performance and significantly reduces the hardware complexity. Secondly, a novel thresholding mechanism for spike detection has been introduced. By incorporating firing rate information as a key determinant in establishing the spike detection threshold, we have improved the adaptiveness of spike detection. This eventually allows the spike detection to overcome the signal degradation that arises due to scar tissue growth around the recording site, thereby ensuring enduringly stable spike detection results. The long-term decoding performance, as a consequence, has also been improved notably. Thirdly, the relationship between spike detection performance and neural decoding accuracy has been investigated to be nonlinear, offering new opportunities for further reducing transmission bandwidth by at least 30% with minor decoding performance degradation. In summary, this thesis presents a journey toward designing ultra-hardware-efficient spike detection algorithms and applying them to reduce the data bandwidth and improve neural decoding performance. The software-hardware co-design approach is essential for the next generation of wireless brain-machine interfaces with increased channel counts and a highly constrained hardware budget. The fundamental aim of this research is to develop methods for high-performance neural spike processing co-designed within low-power hardware that is scaleable for real-time wireless BMI applications. The specific original contributions include the following: Firstly, a new method has been developed for hardware-efficient spike detection, which achieves state-of-the-art spike detection performance and significantly reduces the hardware complexity. Secondly, a novel thresholding mechanism for spike detection has been introduced. By incorporating firing rate information as a key determinant in establishing the spike detection threshold, we have improved the adaptiveness of spike detection. This eventually allows the spike detection to overcome the signal degradation that arises due to scar tissue growth around the recording site, thereby ensuring enduringly stable spike detection results. The long-term decoding performance, as a consequence, has also been improved notably. Thirdly, the relationship between spike detection performance and neural decoding accuracy has been investigated to be nonlinear, offering new opportunities for further reducing transmission bandwidth by at least 30\% with only minor decoding performance degradation. In summary, this thesis presents a journey toward designing ultra-hardware-efficient spike detection algorithms and applying them to reduce the data bandwidth and improve neural decoding performance. The software-hardware co-design approach is essential for the next generation of wireless brain-machine interfaces with increased channel counts and a highly constrained hardware budget.Open Acces

Compressive Sensing and Multichannel Spike Detection for Neuro-Recording Systems

RÉSUMÉ Les interfaces cerveau-machines (ICM) sont de plus en plus importantes dans la recherche biomédicale et ses applications, tels que les tests et analyses médicaux en laboratoire, la cérébrologie et le traitement des dysfonctions neuromusculaires. Les ICM en général et les dispositifs d'enregistrement neuronaux, en particulier, dépendent fortement des méthodes de traitement de signaux utilisées pour fournir aux utilisateurs des renseignements sur l’état de diverses fonctions du cerveau. Les dispositifs d'enregistrement neuronaux courants intègrent de nombreux canaux parallèles produisant ainsi une énorme quantité de données. Celles-ci sont difficiles à transmettre, peuvent manquer une information précieuse des signaux enregistrés et limitent la capacité de traitement sur puce. Une amélioration de fonctions de traitement du signal est nécessaire pour s’assurer que les dispositifs d'enregistrements neuronaux peuvent faire face à l'augmentation rapide des exigences de taille de données et de précision requise de traitement. Cette thèse regroupe deux approches principales de traitement du signal - la compression et la réduction de données - pour les dispositifs d'enregistrement neuronaux. Tout d'abord, l’échantillonnage comprimé (AC) pour la compression du signal neuronal a été utilisé. Ceci implique l’usage d’une matrice de mesure déterministe basée sur un partitionnement selon le minimum de la distance Euclidienne ou celle de la distance de Manhattan (MDC). Nous avons comprimé les signaux neuronaux clairsemmés (Sparse) et non-clairsemmés et les avons reconstruit avec une marge d'erreur minimale en utilisant la matrice MDC construite plutôt. La réduction de données provenant de signaux neuronaux requiert la détection et le classement de potentiels d’actions (PA, ou spikes) lesquelles étaient réalisées en se servant de la méthode d’appariement de formes (templates) avec l'inférence bayésienne (Bayesian inference based template matching - BBTM). Par comparaison avec les méthodes fondées sur l'amplitude, sur le niveau d’énergie ou sur l’appariement de formes, la BBTM a une haute précision de détection, en particulier pour les signaux à faible rapport signal-bruit et peut séparer les potentiels d’actions reçus à partir des différents neurones et qui chevauchent. Ainsi, la BBTM peut automatiquement produire les appariements de formes nécessaires avec une complexité de calculs relativement faible.----------ABSTRACT Brain-Machine Interfaces (BMIs) are increasingly important in biomedical research and health care applications, such as medical laboratory tests and analyses, cerebrology, and complementary treatment of neuromuscular disorders. BMIs, and neural recording devices in particular, rely heavily on signal processing methods to provide users with nformation. Current neural recording devices integrate many parallel channels, which produce a huge amount of data that is difficult to transmit, cannot guarantee the quality of the recorded signals and may limit on-chip signal processing capabilities. An improved signal processing system is needed to ensure that neural recording devices can cope with rapidly increasing data size and accuracy requirements. This thesis focused on two signal processing approaches – signal compression and reduction – for neural recording devices. First, compressed sensing (CS) was employed for neural signal compression, using a minimum Euclidean or Manhattan distance cluster-based (MDC) deterministic sensing matrix. Sparse and non-sparse neural signals were substantially compressed and later reconstructed with minimal error using the built MDC matrix. Neural signal reduction required spike detection and sorting, which was conducted using a Bayesian inference-based template matching (BBTM) method. Compared with amplitude-based, energy-based, and some other template matching methods, BBTM has high detection accuracy, especially for low signal-to-noise ratio signals, and can separate overlapping spikes acquired from different neurons. In addition, BBTM can automatically generate the needed templates with relatively low system complexity. Finally, a digital online adaptive neural signal processing system, including spike detector and CS-based compressor, was designed. Both single and multi-channel solutions were implemented and evaluated. Compared with the signal processing systems in current use, the proposed signal processing system can efficiently compress a large number of sampled data and recover original signals with a small reconstruction error; also it has low power consumption and a small silicon area. The completed prototype shows considerable promise for application in a wide range of neural recording interfaces

Materials and neuroscience: validating tools for large-scale, high-density neural recording

Extracellular recording remains the only technique capable of measuring the activity of many neurons simultaneously with a sub-millisecond precision, in multiple brain areas, including deep structures. Nevertheless, many questions about the nature of the detected signal and the limitations/capabilities of this technique remain unanswered. The general goal of this work is to apply the methodology and concepts of materials science to answer some of the major questions surrounding extracellular recording, and thus take full advantage of this seminal technique. We start out by quantifying the effect of electrode impedance on the amplitude of measured extracellular spikes and background noise. Can we improve data quality by lowering electrode impedance? We demonstrate that if the proper recording system is used, then the impedance of a microelectrode, within the range typical of standard polytrodes (~ 0.1 to 2 MΩ), does not significantly affect a neural spike amplitude or the background noise, and therefore spike sorting. In addition to improving the performance of each electrode, increasing the number of electrodes in a single neural probe has also proven advantageous for simultaneously monitoring the activity of more neurons with better spatiotemporal resolution. How can we achieve large-scale, highdensity extracellular recordings without compromising brain tissue? Here we report the design and in vivo validation of a complementary metal–oxide–semiconductor (CMOS)-based scanning probe with 1356 electrodes arranged along approximately 8 mm of a thin shaft (50 μm thick and 100 μm wide). Additionally, given the ever-shrinking dimensions of CMOS technology, there is a drive to fabricate sub-cellular electrodes (< 10 μm). Therefore, to evaluate electrode configurations for future probe designs, several recordings from many different brain regions were performed with an ultra-dense probe containing 255 electrodes, each with a geometric area of 5 x 5 μm and a pitch of 6 μm. How can we validate neural probes with different electrode materials/configurations and different sorting algorithms? We describe a new procedure for precisely aligning two probes for in vivo “paired-recordings” such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micro-pipette. We gathered a dataset of paired-recordings, which is available online. The “ground truth” data, for which one knows exactly when a neuron in the vicinity of an extracellular probe generates an action potential, has been used for several groups to validate and quantify the performance of new algorithms to automatically detect/sort single-units

Exploiting All-Programmable System on Chips for Closed-Loop Real-Time Neural Interfaces

High-density microelectrode arrays (HDMEAs) feature thousands of recording electrodes in a single chip with an area of few square millimeters. The obtained electrode density is comparable and even higher than the typical density of neuronal cells in cortical cultures. Commercially available HDMEA-based acquisition systems are able to record the neural activity from the whole array at the same time with submillisecond resolution. These devices are a very promising tool and are increasingly used in neuroscience to tackle fundamental questions regarding the complex dynamics of neural networks. Even if electrical or optical stimulation is generally an available feature of such systems, they lack the capability of creating a closed-loop between the biological neural activity and the artificial system. Stimuli are usually sent in an open-loop manner, thus violating the inherent working basis of neural circuits that in nature are constantly reacting to the external environment. This forbids to unravel the real mechanisms behind the behavior of neural networks. The primary objective of this PhD work is to overcome such limitation by creating a fullyreconfigurable processing system capable of providing real-time feedback to the ongoing neural activity recorded with HDMEA platforms. The potentiality of modern heterogeneous FPGAs has been exploited to realize the system. In particular, the Xilinx Zynq All Programmable System on Chip (APSoC) has been used. The device features reconfigurable logic, specialized hardwired blocks, and a dual-core ARM-based processor; the synergy of these components allows to achieve high elaboration performances while maintaining a high level of flexibility and adaptivity. The developed system has been embedded in an acquisition and stimulation setup featuring the following platforms: \u2022 3\ub7Brain BioCam X, a state-of-the-art HDMEA-based acquisition platform capable of recording in parallel from 4096 electrodes at 18 kHz per electrode. \u2022 PlexStim\u2122 Electrical Stimulator System, able to generate electrical stimuli with custom waveforms to 16 different output channels. \u2022 Texas Instruments DLP\uae LightCrafter\u2122 Evaluation Module, capable of projecting 608x684 pixels images with a refresh rate of 60 Hz; it holds the function of optical stimulation. All the features of the system, such as band-pass filtering and spike detection of all the recorded channels, have been validated by means of ex vivo experiments. Very low-latency has been achieved while processing the whole input data stream in real-time. In the case of electrical stimulation the total latency is below 2 ms; when optical stimuli are needed, instead, the total latency is a little higher, being 21 ms in the worst case. The final setup is ready to be used to infer cellular properties by means of closed-loop experiments. As a proof of this concept, it has been successfully used for the clustering and classification of retinal ganglion cells (RGCs) in mice retina. For this experiment, the light-evoked spikes from thousands of RGCs have been correctly recorded and analyzed in real-time. Around 90% of the total clusters have been classified as ON- or OFF-type cells. In addition to the closed-loop system, a denoising prototype has been developed. The main idea is to exploit oversampling techniques to reduce the thermal noise recorded by HDMEAbased acquisition systems. The prototype is capable of processing in real-time all the input signals from the BioCam X, and it is currently being tested to evaluate the performance in terms of signal-to-noise-ratio improvement

Resource-efficient algorithms and circuits for highly-scalable BMI channel architectures

The study of the human brain has for long fascinated mankind. This organ that controls all cognitive processes and physical actions remains, to this day, among the least understood biological systems. Several billions of neurons form intricate interconnected networks communicating information through through complex electrochemical activities. Electrode arrays, such as for EEG, ECoG, and MEAs (microelectrode arrays), have enabled the observation of neural activity through recording of these electrical signals for both investigative and clinical applications. Although MEAs are widely considered the most invasive such method for recording, they do however provide highest resolution (both spatially and temporally). Due to close proximity, each microelectrode can pick up spiking activity from multiple neurons. This thesis focuses on the design and implementation of novel circuits and systems suitable for high channel count implantable neural interfaces. Implantability poses stringent requirements on the design, such as ultra-low power, small silicon footprint, reduced communication bandwidth and high efficiency to avoid information loss. The information extraction chain typically involves signal amplification and conditioning, spike detection, and spike sorting to determine the spatial and time firing pattern of each neuron. This thesis first provides a background to the origin and basic electrophysiology of these biopotential signals followed by a thorough review of the relevant state-of-the circuits and systems for facilitating the neural interface. Within this context, novel front-end circuits are presented for achieving resource-constrained biopotential amplification whilst additionally considering the signal dynamics and realistic requirements for effective classification. Specifically, it is shown how a band-limited biopotential amplifier can reduce power requirements without compromising detectability. Furthermore through the development of a novel automatic gain control for neural spike recording, the dynamic range of the signal in subsequent processing blocks can be maintained in multichannel systems. This is particularly effective if now considering systems that no longer requiring independent tuning of amplification gains for each individual channel. This also alleviates the common requirement to over-spec the resolution in data conversion therefore saving power, area and data capacity. Dealing with basic spike detection and feature extraction, a novel circuit for maxima detection is presented for identifying and signalling the onset of spike peaks and troughs. This is then combined with a novel non-linear energy operator (NEO) preprocessor and applied to spike detection. This again contributes to the general theme of achieving a calibration-free multi-channel system that is signal-driven and adaptive. Another original contribution herein includes a spike rate encoder circuit suitable for applications that are not are not affected by providing multi-unit responses. Finally, spike sorting (feature extraction and clustering) is examined. A new method for feature extraction is proposed based on utilising the extrema of the first and second derivatives of the signal. It is shown that this provides an extremely resource-efficient metric than can achieve noise immunity than other methods of comparable complexity. Furthermore, a novel unsupervised clustering method is proposed which adaptively determines the number of clusters and assigns incoming spikes to appropriate cluster on-the-fly. In addition to high accuracy achieved by the combination of these methods for spike sorting, a major advantage is their low-computational complexity that renders them readily implementable in low-power hardware.Open Acces

Real-time signal detection and classification algorithms for body-centered systems

El principal motivo por el cual los sistemas de comunicación en el entrono corporal se desean con el objetivo de poder obtener y procesar señales biométricas para monitorizar e incluso tratar una condición médica sea ésta causada por una enfermedad o el rendimiento de un atleta. Dado que la base de estos sistemas está en la sensorización y el procesado, los algoritmos de procesado de señal son una parte fundamental de los mismos. Esta tesis se centra en los algoritmos de tratamiento de señales en tiempo real que se utilizan tanto para monitorizar los parámetros como para obtener la información que resulta relevante de las señales obtenidas. En la primera parte se introduce los tipos de señales y sensores en los sistemas en el entrono corporal. A continuación se desarrollan dos aplicaciones concretas de los sistemas en el entorno corporal así como los algoritmos que en las mismas se utilizan. La primera aplicación es el control de glucosa en sangre en pacientes con diabetes. En esta parte se desarrolla un método de detección mediante clasificación de patronones de medidas erróneas obtenidas con el monitor contínuo comercial "Minimed CGMS". La segunda aplicacióin consiste en la monitorizacióni de señales neuronales. Descubrimientos recientes en este campo han demostrado enormes posibilidades terapéuticas (por ejemplo, pacientes con parálisis total que son capaces de comunicarse con el entrono gracias a la monitorizacióin e interpretación de señales provenientes de sus neuronas) y también de entretenimiento. En este trabajo, se han desarrollado algoritmos de detección, clasificación y compresión de impulsos neuronales y dichos algoritmos han sido evaluados junto con técnicas de transmisión inalámbricas que posibiliten una monitorización sin cables. Por último, se dedica un capítulo a la transmisión inalámbrica de señales en los sistemas en el entorno corporal. En esta parte se estudia las condiciones del canal que presenta el entorno corporal para la transmisión de sTraver Sebastiá, L. (2012). Real-time signal detection and classification algorithms for body-centered systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16188Palanci