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

A Closed-Loop Bidirectional Brain-Machine Interface System For Freely Behaving Animals

A brain-machine interface (BMI) creates an artificial pathway between the brain and the external world. The research and applications of BMI have received enormous attention among the scientific community as well as the public in the past decade. However, most research of BMI relies on experiments with tethered or sedated animals, using rack-mount equipment, which significantly restricts the experimental methods and paradigms. Moreover, most research to date has focused on neural signal recording or decoding in an open-loop method. Although the use of a closed-loop, wireless BMI is critical to the success of an extensive range of neuroscience research, it is an approach yet to be widely used, with the electronics design being one of the major bottlenecks. The key goal of this research is to address the design challenges of a closed-loop, bidirectional BMI by providing innovative solutions from the neuron-electronics interface up to the system level. Circuit design innovations have been proposed in the neural recording front-end, the neural feature extraction module, and the neural stimulator. Practical design issues of the bidirectional neural interface, the closed-loop controller and the overall system integration have been carefully studied and discussed.To the best of our knowledge, this work presents the first reported portable system to provide all required hardware for a closed-loop sensorimotor neural interface, the first wireless sensory encoding experiment conducted in freely swimming animals, and the first bidirectional study of the hippocampal field potentials in freely behaving animals from sedation to sleep. This thesis gives a comprehensive survey of bidirectional BMI designs, reviews the key design trade-offs in neural recorders and stimulators, and summarizes neural features and mechanisms for a successful closed-loop operation. The circuit and system design details are presented with bench testing and animal experimental results. The methods, circuit techniques, system topology, and experimental paradigms proposed in this work can be used in a wide range of relevant neurophysiology research and neuroprosthetic development, especially in experiments using freely behaving animals