Artifact-Aware Analogue/Mixed-Signal Front-Ends for Neural Recording Applications

This paper presents a brief review of techniques to overcome the problems associated with artifacts in analog frontends for neural recording applications. These techniques are employed for handling Common-Mode (CM) Differential-Mode (DM) artifacts and include techniques such as Average Template Subtraction, Channel Blanking or Blind Adaptive Stimulation Artifact Rejection (ASAR), among others. Additionally, a new technique for DM artifacts compression is proposed. It allows to compress these artifacts to the requirements of the analog frontend and, afterwards, it allows to reconstruct the whole artifact or largely suppress it.Ministerio de Economía y Empresa TEC2016-80923-

A 32 Input Multiplexed Channel Analog Front-End with Spatial Delta Encoding Technique and Differential Artifacts Compression

This paper describes a low-noise, low-power and high dynamic range analog front-end intended for sensing neural signals. In order to reduce interface area, a 32-channel multiplexer is implemented on circuit input. Furthermore, a spatial delta encoding is proposed to compress the signal range. A differential artifact compression algorithm is implemented to avoid saturation in the signal path, thus enabling reconstruct or suppressing artifacts in digital domain. The proposed design has been implemented using 0.18 μm TSMC technology. Experimental results shows a power consumption per channel of 1.0 μW, an input referred noise of 1.1 μVrms regarding the bandwidth of interest and a dynamic range of 91 dB.Ministerio de Economía y Competitividad TEC2016-80923-POffice of Naval Research ONR N00014- 19-1-215

A 32-Channel Time-Multiplexed Artifact-Aware Neural Recording System

This paper presents a low-power, low-noise microsystem for the recording of neural local field potentials or intracranial electroencephalographic signals. It features 32 time-multiplexed channels at the electrode interface and offers the possibility to spatially delta encode data to take advantage of the large correlation of signals captured from nearby channels. The circuit also implements a mixed-signal voltage-triggered auto-ranging algorithm which allows to attenuate large interferers in digital domain while preserving neural information. This effectively increases the system dynamic range and avoids the onset of saturation. A prototype, fabricated in a standard 180 nm CMOS process, has been experimentally verified in-vitro with cellular cultures of primary cortical neurons from mice. The system shows an integrated input-referred noise in the 0.5–200 Hz band of 1.4 µVrms for a spot noise of about 85 nV / √Hz. The system draws 1.5 µW per channel from 1.2 V supply and obtains 71 dB + 26 dB dynamic range when the artifact-aware auto-ranging mechanism is enabled, without penalising other critical specifications such as crosstalk between channels or common-mode and power supply rejection ratios

A Consumer-tier based Visual-Brain Machine Interface for Augmented Reality Glasses Interactions

Objective.Visual-Brain Machine Interface(V-BMI) has provide a novel interaction technique for Augmented Reality (AR) industries. Several state-of-arts work has demonstates its high accuracy and real-time interaction capbilities. However, most of the studies employ EEGs devices that are rigid and difficult to apply in real-life AR glasseses application sceniraros. Here we develop a consumer-tier Visual-Brain Machine Inteface(V-BMI) system specialized for Augmented Reality(AR) glasses interactions. Approach. The developed system consists of a wearable hardware which takes advantages of fast set-up, reliable recording and comfortable wearable experience that specificized for AR glasses applications. Complementing this hardware, we have devised a software framework that facilitates real-time interactions within the system while accommodating a modular configuration to enhance scalability. Main results. The developed hardware is only 110g and 120x85x23 mm, which with 1 Tohm and peak to peak voltage is less than 1.5 uV, and a V-BMI based angry bird game and an Internet of Thing (IoT) AR applications are deisgned, we demonstrated such technology merits of intuitive experience and efficiency interaction. The real-time interaction accuracy is between 85 and 96 percentages in a commercial AR glasses (DTI is 2.24s and ITR 65 bits-min ). Significance. Our study indicates the developed system can provide an essential hardware-software framework for consumer based V-BMI AR glasses. Also, we derive several pivotal design factors for a consumer-grade V-BMI-based AR system: 1) Dynamic adaptation of stimulation patterns-classification methods via computer vision algorithms is necessary for AR glasses applications; and 2) Algorithmic localization to foster system stability and latency reduction.Comment: 15 pages,10 figure

Anti-artifacts techniques for neural recording front-ends in closed-loop brain-machine interface ICs

In recent years, thanks to the development of integrated circuits, clinical medicine has witnessed significant advancements, enabling more efficient and intelligent treatment approaches. Particularly in the field of neuromedical, the utilization of brain-machine interfaces (BMI) has revolutionized the treatment of neurological diseases such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. The BMI acquires neural signals via recording circuits and analyze them to regulate neural stimulator circuits for effective neurological treatment. However, traditional BMI designs, which are often isolated, have given way to closed-loop brain-machine interfaces (CL-BMI) as a contemporary development trend. CL-BMI offers increased integration and accelerated response speed, marking a significant leap forward in neuromedicine. Nonetheless, this advancement comes with its challenges, notably the stimulation artifacts (SA) problem inherent to the structural characteristics of CL-BMI, which poses significant challenges on the neural recording front-ends (NRFE) site. This paper aims to provide a comprehensive overview of technologies addressing artifacts in the NRFE site within CL-BMI. Topics covered will include: (1) understanding and assessing artifacts; (2) exploring the impact of artifacts on traditional neural recording front-ends; (3) reviewing recent technological advancements aimed at addressing artifact-related issues; (4) summarizing and classifying the aforementioned technologies, along with an analysis of future trends

Cultural Context-Aware Models and IT Applications for the Exploitation of Musical Heritage

Information engineering has always expanded its scope by inspiring innovation in different scientific disciplines. In particular, in the last sixty years, music and engineering have forged a strong connection in the discipline known as “Sound and Music Computing”. Musical heritage is a paradigmatic case that includes several multi-faceted cultural artefacts and traditions. Several issues arise from the analog-digital transfer of cultural objects, concerning their creation, preservation, access, analysis and experiencing. The keystone is the relationship of these digitized cultural objects with their carrier and cultural context. The terms “cultural context” and “cultural context awareness” are delineated, alongside the concepts of contextual information and metadata. Since they maintain the integrity of the object, its meaning and cultural context, their role is critical. This thesis explores three main case studies concerning historical audio recordings and ancient musical instruments, aiming to delineate models to preserve, analyze, access and experience the digital versions of these three prominent examples of musical heritage. The first case study concerns analog magnetic tapes, and, in particular, tape music, a particular experimental music born in the second half of the XX century. This case study has relevant implications from the musicology, philology and archivists’ points of view, since the carrier has a paramount role and the tight connection with its content can easily break during the digitization process or the access phase. With the aim to help musicologists and audio technicians in their work, several tools based on Artificial Intelligence are evaluated in tasks such as the discontinuity detection and equalization recognition. By considering the peculiarities of tape music, the philological problem of stemmatics in digitized audio documents is tackled: an algorithm based on phylogenetic techniques is proposed and assessed, confirming the suitability of these techniques for this task. Then, a methodology for a historically faithful access to digitized tape music recordings is introduced, by considering contextual information and its relationship with the carrier and the replay device. Based on this methodology, an Android app which virtualizes a tape recorder is presented, together with its assessment. Furthermore, two web applications are proposed to faithfully experience digitized 78 rpm discs and magnetic tape recordings, respectively. Finally, a prototype of web application for musicological analysis is presented. This aims to concentrate relevant part of the knowledge acquired in this work into a single interface. The second case study is a corpus of Arab-Andalusian music, suitable for computational research, which opens new opportunities to musicological studies by applying data-driven analysis. The description of the corpus is based on the five criteria formalized in the CompMusic project of the University Pompeu Fabra of Barcelona: purpose, coverage, completeness, quality and re-usability. Four Jupyter notebooks were developed with the aim to provide a useful tool for computational musicologists for analyzing and using data and metadata of such corpus. The third case study concerns an exceptional historical musical instrument: an ancient Pan flute exhibited at the Museum of Archaeological Sciences and Art of the University of Padova. The final objective was the creation of a multimedia installation to valorize this precious artifact and to allow visitors to interact with the archaeological find and to learn its history. The case study provided the opportunity to study a methodology suitable for the valorization of this ancient musical instrument, but also extendible to other artifacts or museum collections. Both the methodology and the resulting multimedia installation are presented, followed by the assessment carried out by a multidisciplinary group of experts

Closed-loop approaches for innovative neuroprostheses

The goal of this thesis is to study new ways to interact with the nervous system in case of damage or pathology. In particular, I focused my effort towards the development of innovative, closed-loop stimulation protocols in various scenarios: in vitro, ex vivo, in vivo

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods