296 research outputs found
Accurate, Very Low Computational Complexity Spike Sorting Using Unsupervised Matched Subspace Learning
This paper presents an adaptable dictionary-based feature extraction approach for spike sorting offering high accuracy and low computational complexity for implantable applications. It extracts and learns identifiable features from evolving subspaces through matched unsupervised subspace filtering. To provide compatibility with the strict constraints in implantable devices such as the chip area and power budget, the dictionary contains arrays of {-1, 0 and 1} and the algorithm need only process addition and subtraction operations. Three types of such dictionary were considered. To quantify and compare the performance of the resulting three feature extractors with existing systems, a neural signal simulator based on several different libraries was developed. For noise levels between 0.05 and 0.3 and groups of 3 to 6 clusters, all three feature extractors provide robust high performance with average classification errors of less than 8% over five iterations, each consisting of 100 generated data segments. To our knowledge, the proposed adaptive feature extractors are the first able to classify reliably 6 clusters for implantable applications. An ASIC implementation of the best performing dictionary-based feature extractor was synthesized in a 65-nm CMOS process. It occupies an area of 0.09 mm2 and dissipates up to about 10.48 ÎŒW from a 1 V supply voltage, when operating with 8-bit resolution at 30 kHz operating frequency
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
Compressive Sensing and Multichannel Spike Detection for Neuro-Recording Systems
RĂSUMĂ Les interfaces cerveau-machines (ICM) sont de plus en plus importantes dans la recherche biomĂ©dicale et ses applications, tels que les tests et analyses mĂ©dicaux en laboratoire, la cĂ©rĂ©brologie et le traitement des dysfonctions neuromusculaires. Les ICM en gĂ©nĂ©ral et les dispositifs d'enregistrement neuronaux, en particulier, dĂ©pendent fortement des mĂ©thodes de traitement de signaux utilisĂ©es pour fournir aux utilisateurs des renseignements sur lâĂ©tat de diverses fonctions du cerveau. Les dispositifs d'enregistrement neuronaux courants intĂšgrent de nombreux canaux parallĂšles produisant ainsi une Ă©norme quantitĂ© de donnĂ©es. Celles-ci sont difficiles Ă transmettre, peuvent manquer une information prĂ©cieuse des signaux enregistrĂ©s et limitent la capacitĂ© de traitement sur puce. Une amĂ©lioration de fonctions de traitement du signal est nĂ©cessaire pour sâassurer que les dispositifs d'enregistrements neuronaux peuvent faire face Ă l'augmentation rapide des exigences de taille de donnĂ©es et de prĂ©cision requise de traitement. Cette thĂšse regroupe deux approches principales de traitement du signal - la compression et la rĂ©duction de donnĂ©es - pour les dispositifs d'enregistrement neuronaux. Tout d'abord, lâĂ©chantillonnage comprimĂ© (AC) pour la compression du signal neuronal a Ă©tĂ© utilisĂ©. Ceci implique lâusage dâune matrice de mesure dĂ©terministe basĂ©e sur un partitionnement selon le minimum de la distance Euclidienne ou celle de la distance de Manhattan (MDC). Nous avons comprimĂ© les signaux neuronaux clairsemmĂ©s (Sparse) et non-clairsemmĂ©s et les avons reconstruit avec une marge d'erreur minimale en utilisant la matrice MDC construite plutĂŽt.
La rĂ©duction de donnĂ©es provenant de signaux neuronaux requiert la dĂ©tection et le classement de potentiels dâactions (PA, ou spikes) lesquelles Ă©taient rĂ©alisĂ©es en se servant de la mĂ©thode dâappariement de formes (templates) avec l'infĂ©rence bayĂ©sienne (Bayesian inference based template matching - BBTM). Par comparaison avec les mĂ©thodes fondĂ©es sur l'amplitude, sur le niveau dâĂ©nergie ou sur lâappariement de formes, la BBTM a une haute prĂ©cision de dĂ©tection, en particulier pour les signaux Ă faible rapport signal-bruit et peut sĂ©parer les potentiels dâactions reçus Ă partir des diffĂ©rents neurones et qui chevauchent. Ainsi, la BBTM peut automatiquement produire les appariements de formes nĂ©cessaires avec une complexitĂ© 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 â signal compression and reduction â 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
Biometric Systems
Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study
A Data-Analysis and Sensitivity-Optimization Framework for the KATRIN Experiment
Presently under construction, the Karlsruhe TRitium Neutrino (KATRIN) experiment is the next generation tritium beta-decay experiment to perform a direct kinematical measurement of the electron neutrino mass with an unprecedented sensitivity of 200 meV (90% C.L.). This thesis describes the implementation of a consistent data analysis framework, addressing technical aspects of the data taking process and statistical challenges of a neutrino mass estimation from the beta-decay electron spectrum
3D Robotic Sensing of People: Human Perception, Representation and Activity Recognition
The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives.
As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical human-centered robotics applications.
This research focuses on robotic sensing of people, that is, how robots can perceive and represent humans and understand their behaviors, primarily through 3D robotic vision. In this dissertation, I begin with a broad perspective on human-centered robotics by discussing its real-world applications and significant challenges. Then, I will introduce a real-time perception system, based on the concept of Depth of Interest, to detect and track multiple individuals using a color-depth camera that is installed on moving robotic platforms. In addition, I will discuss human representation approaches, based on local spatio-temporal features, including new âCoDe4Dâ features that incorporate both color and depth information, a new âSODâ descriptor to efficiently quantize 3D visual features, and the novel AdHuC features, which are capable of representing the activities of multiple individuals. Several new algorithms to recognize human activities are also discussed, including the RG-PLSA model, which allows us to discover activity patterns without supervision, the MC-HCRF model, which can explicitly investigate certainty in latent temporal patterns, and the FuzzySR model, which is used to segment continuous data into events and probabilistically recognize human activities. Cognition models based on recognition results are also implemented for decision making that allow robotic systems to react to human activities. Finally, I will conclude with a discussion of future directions that will accelerate the upcoming technological revolution of human-centered robotics
Intelligent Biosignal Processing in Wearable and Implantable Sensors
This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brainâmachine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine
Heterogeneous recognition of bioacoustic signals for human-machine interfaces
Human-machine interfaces (HMI) provide a communication pathway between
man and machine. Not only do they augment existing pathways, they can substitute
or even bypass these pathways where functional motor loss prevents the
use of standard interfaces. This is especially important for individuals who rely
on assistive technology in their everyday life. By utilising bioacoustic activity,
it can lead to an assistive HMI concept which is unobtrusive, minimally disruptive
and cosmetically appealing to the user. However, due to the complexity of
the signals it remains relatively underexplored in the HMI field.
This thesis investigates extracting and decoding volition from bioacoustic activity
with the aim of generating real-time commands. The developed framework
is a systemisation of various processing blocks enabling the mapping of continuous
signals into M discrete classes. Class independent extraction efficiently
detects and segments the continuous signals while class-specific extraction exemplifies
each pattern set using a novel template creation process stable to
permutations of the data set. These templates are utilised by a generalised
single channel discrimination model, whereby each signal is template aligned
prior to classification. The real-time decoding subsystem uses a multichannel
heterogeneous ensemble architecture which fuses the output from a diverse set
of these individual discrimination models. This enhances the classification performance
by elevating both the sensitivity and specificity, with the increased
specificity due to a natural rejection capacity based on a non-parametric majority
vote. Such a strategy is useful when analysing signals which have diverse
characteristics, false positives are prevalent and have strong consequences, and
when there is limited training data available. The framework has been developed
with generality in mind with wide applicability to a broad spectrum of
biosignals.
The processing system has been demonstrated on real-time decoding of tongue-movement
ear pressure signals using both single and dual channel setups. This
has included in-depth evaluation of these methods in both offline and online
scenarios. During online evaluation, a stimulus based test methodology was
devised, while representative interference was used to contaminate the decoding
process in a relevant and real fashion. The results of this research
provide a strong case for the utility of such techniques in real world applications
of human-machine communication using impulsive bioacoustic signals
and biosignals in general
Multi-electrode array recording and data analysis methods for molluscan central nervous systems
In this work the use of the central nervous system (CNS) of the aquatic
snail Lymnaea stagnalis on planar multi-electrode arrays (MEAs) was
developed and analysis methods for the data generated were created.
A variety of different combinations of configurations of tissue from the
Lymnaea CNS were explored to determine the signal characteristics
that could be recorded by sixty channel MEAs. In particular, the
suitability of the semi-intact system consisting of the lips, oesophagus,
CNS, and associated nerve connectives was developed for use on
the planar MEA. The recording target area of the dorsal surface of
the buccal ganglia was selected as being the most promising for study
and recordings of its component cells during fictive feeding behaviour
stimulated by sucrose were made. The data produced by this type of
experimentation is very high volume and so its analysis required the
development of a custom set of software tools. The goal of this tool
set is to find the signal from individual neurons in the data streams of
the electrodes of a planar MEA, to estimate their position, and then
to predict their causal connectivity. To produce such an analysis techniques
for noise filtration, neural spike detection, and group detection
of bursts of spikes were created to pre-process electrode data streams.
The Kohonen self-organising map (SOM) algorithm was adapted for
the purpose of separating detected spikes into data streams representing
the spike output of individual cells found in the target system. A
significant addition to SOM algorithm was developed by the concurrent
use of triangulation methods based on current source density
analysis to predict the position of individual cells based on their spike
output on more than one electrode. The likely functional connectivity
of individual neurons identified by the SOM technique were analysed
through the use of a statistical causality method known as Granger
causality/causal connectivity. This technique was used to produce a
map of the likely connectivity between neural sources
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