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

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

Advances in Bioengineering

The technological approach and the high level of innovation make bioengineering extremely dynamic and this forces researchers to continuous updating. It involves the publication of the results of the latest scientific research. This book covers a wide range of aspects and issues related to advances in bioengineering research with a particular focus on innovative technologies and applications. The book consists of 13 scientific contributions divided in four sections: Materials Science; Biosensors. Electronics and Telemetry; Light Therapy; Computing and Analysis Techniques

INTEGRATION OF CMOS TECHNOLOGY INTO LAB-ON-CHIP SYSTEMS APPLIED TO THE DEVELOPMENT OF A BIOELECTRONIC NOSE

This work addresses the development of a lab-on-a-chip (LOC) system for olfactory sensing. The method of sensing employed is cell-based, utilizing living cells to sense stimuli that are otherwise not easily sensed using conventional transduction techniques. Cells have evolved over millions of years to be exquisitely sensitive to their environment, with certain types of cells producing electrical signals in response to stimuli. The core device that is introduced here is comprised of living olfactory sensory neurons (OSNs) on top of a complementary metal-oxide-semiconductor (CMOS) integrated circuit (IC). This hybrid bioelectronic approach to sensing leverages the sensitivity of OSNs with the electronic signal processing capability of modern ICs. Intimately combining electronics with biology presents a number of unique challenges to integration that arise from the disparate requirements of the two separate domains. Fundamentally the obstacles arise from the facts that electronic devices are designed to work in dry environments while biology requires not only a wet environment, but also one that is precisely controlled and non-toxic. Design and modeling of such heterogeneously integrated systems is complicated by the lack of tools that can address the multiple domains and techniques required for integration, namely IC design, fluidics, packaging, and microfabrication, and cell culture. There also arises the issue of how to handle the vast amount of data that can be generated by such systems, and specifically how to efficiently identify signals of interest and communicate them off-chip. The primary contributions of this work are the development of a new packaging scheme for integration of CMOS ICs into fluidic LOC systems, a methodology for cross-coupled multi-domain iterative modeling of heterogeneously integrated systems, demonstration of a proof-of-concept bioelectronic olfactory sensor, and a novel event-based technique to minimize the bandwidth required to communicate the information contained in bio-potential signals produced by dense arrays of electrically active cells

Systèmes intégrés pour l'hybridation vivant-artificiel : modélisation et conception d'une chaine de détection analogique adaptative

Version corrigée/Bioelectronics is a transdisciplinary field which develops interconnection devices between biological systems presenting electrical activity and the world of electronics. This communication with living tissues implies to observe the electrical activity of the cells and therefore requires an electronic acquisition chain.The use of Multi / Micro Electrode Array leads to systems that acquire a large numberof parallel channels, thus consumption and congestion of acquisition circuits have a significant impact on the viability of the system to be implanted.This thesis proposes two reflections about these acquisition circuits. One of these reflections relates to amplifier circuits, their input impedance and consumption; the other concerns an analogue action potentials detector, its modeling and optimization.These theoretical work leading to concrete results, an ASIC was designed, manufactured, tested and characterized in this thesis. This eight-channel ASIC therefore includes amplifiers and analogue action potentials detector and is the main contribution of this thesis.La bioélectronique est un domaine transdisciplinaire qui oeuvre, entre autres, àl’interconnexion entre des systèmes biologiques présentant une activité électrique et le monde de l’électronique. Cette communication avec le vivant implique l’observation de l’activité électrique des cellules considérées et nécessite donc une chaine d’acquisition électronique.L’utilisation de Multi/Micro Electrodes Array débouche sur des systèmes devant acquérir un grand nombre de canaux en parallèle, dès lors la consommation et l’encombrement des circuits d’acquisition ont un impact significatif sur la viabilité du système destiné à être implanté.Cette thèse propose deux réflexions à propos de ces circuits d’acquisition. Une ces des réflexions a trait aux circuits d’amplification, à leur impédance d’entrée et à leur consommation ; l’autre concerne un détecteur de potentiels d’action analogique, sa modélisation et son optimisation.Ces travaux théoriques ayant abouti à des résultats concrets, un ASIC a été conçu,fabriqué, testé et caractérisé au cours de cette thèse. Cet ASIC à huit canaux comporte donc des amplificateurs et des détecteurs de potentiels d’action analogiques et constitue le principal apport de ce travail de thèse

Biomedical Engineering

Biomedical engineering is currently relatively wide scientific area which has been constantly bringing innovations with an objective to support and improve all areas of medicine such as therapy, diagnostics and rehabilitation. It holds a strong position also in natural and biological sciences. In the terms of application, biomedical engineering is present at almost all technical universities where some of them are targeted for the research and development in this area. The presented book brings chosen outputs and results of research and development tasks, often supported by important world or European framework programs or grant agencies. The knowledge and findings from the area of biomaterials, bioelectronics, bioinformatics, biomedical devices and tools or computer support in the processes of diagnostics and therapy are defined in a way that they bring both basic information to a reader and also specific outputs with a possible further use in research and development

11th International Coral Reef Symposium Proceedings

A defining theme of the 11th International Coral Reef Symposium was that the news for coral reef ecosystems are far from encouraging. Climate change happens now much faster than in an ice-age transition, and coral reefs continue to suffer fever-high temperatures as well as sour ocean conditions. Corals may be falling behind, and there appears to be no special silver bullet remedy. Nevertheless, there are hopeful signs that we should not despair. Reef ecosystems respond vigorously to protective measures and alleviation of stress. For concerned scientists, managers, conservationists, stakeholders, students, and citizens, there is a great role to play in continuing to report on the extreme threat that climate change represents to earth’s natural systems. Urgent action is needed to reduce CO2 emissions. In the interim, we can and must buy time for coral reefs through increased protection from sewage, sediment, pollutants, overfishing, development, and other stressors, all of which we know can damage coral health. The time to act is now. The canary in the coral-coal mine is dead, but we still have time to save the miners. We need effective management rooted in solid interdisciplinary science and coupled with stakeholder buy in, working at local, regional, and international scales alongside global efforts to give reefs a chance.https://nsuworks.nova.edu/occ_icrs/1000/thumbnail.jp