Recent Advances in Neural Recording Microsystems

The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field

Structured Dictionary Learning and its applications in Neural Recording

Widely utilized in the field of neuroscience, implantable neural recording devices could capture neuron activities with an acquisition rate on the order of megabytes per second. In order to efficiently transmit neural signals through wireless channels, these devices require compression methods that reduce power consumption. Although recent Compressed Sensing (CS) approaches have successfully demonstrated their power, their full potential is yet to be explored, particularly towards exploring a more efficient representation of the neural signals. As a promising solution, sparse representation not only provides better signal compression for bandwidth/storage efficiency, but also leads to faster processing algorithms as well as more effective signal separation for classification purpose. However, current sparsity‐based approaches for neural recording are limited due to several critical drawbacks: (i) the lack of an efficient data‐driven representation to fully capture the characteristics of specific neural signal; (ii) most existing methods do not fully explore the prior knowledge of neural signals (e.g., labels), while such information is often known; and (iii) the capability to encode discriminative information into the representation to promote classification. Using neural recording as a case study, this dissertation presents new theoretical ideas and mathematical frameworks on structured dictionary learning with applications in compression and classification. Start with a single task setup, we provide theoretical proofs to show the benefits of using structured sparsity in dictionary learning. Then we provide various novel models for the representation of a single measurement, as well as multiple measurements where signals exhibit both with‐in class similarity as well as with‐in class difference. Under the assumption that the label information of the neural signal is known, the proposed models minimize the data fidelity term together with the structured sparsity terms to drive for more discriminative representation. We demonstrate that this is particularly essential in neural recording since it can further improve the compression ratio, classification accuracy and help deal with non‐ideal scenarios such as co-occurrences of neuron firings. Fast and efficient algorithms based on Bayesian inference and alternative direction method are proposed. Extensive experiments are conducted on both neural recording applications as well as some other classification task, such as image classification

Untersuchung von Verarbeitungsalgorithmen zur automatischen Auswertung neuronaler Signale aus Multielektroden-Arrays

Mit Hilfe von Multielektroden-Arrays (MEAs) können viele Zellen gleichzeitig kontaktiert und deren elektrische Aktivität abgeleitet werden. Für die weitere Analyse müssen die abgeleiteten Signale in ihre Einzelbestandteile zerlegt werden. Dieser Vorgang wird als Spike Sorting bezeichnet. In der vorliegenden Arbeit werden Ansätze für ein vollständig automatisiertes Spike Sorting vorgestellt und untersucht. Dabei werden Verfahren aufgezeigt, die mit Hilfe von adaptiven Verfahren die abgeleiteten Zellsignale optimal filtern und automatisch in deren Einzelkomponenten zerlegen

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 brain–machine 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