A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 μm SOI CMOS

In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-μm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 μm wide, 10 mm long, 20 μm thick), achieving a crosstalk of −64.4 dB. The probe base (5 × 9 mm2) implements dual-band recording and a 1

A Wireless Power Transfer Based Implantable ECG Monitoring Device

Implantable medical devices (IMDs) enable patients to monitor their health anytime and receive treatment anywhere. However, due to the limited capacity of a battery, their functionalities are restricted, and the devices may not achieve their intended potential fully. The most promising way to solve this limited capacity problem is wireless power transfer (WPT) technology. In this study, a WPT based implantable electrocardiogram (ECG) monitoring device that continuously records ECG data has been proposed, and its effectiveness is verified through an animal experiment using a rat model. Our proposed device is designed to be of size 24 x 27 x 8 mm, and it is small enough to be implanted in the rat. The device transmits data continuously using a low power Bluetooth Low Energy (BLE) communication technology. To charge the battery wirelessly, transmitting (Tx) and receiving (Rx) antennas were designed and fabricated. The animal experiment results clearly showed that our WPT system enables the device to monitor the ECG of a heart in various conditions continuously, while transmitting all ECG data in real-time.11Ysciescopu

Wireless tools for neuromodulation

Epilepsy is a spectrum of diseases characterized by recurrent seizures. It is estimated that 50 million individuals worldwide are affected and 30% of cases are medically refractory or drug resistant. Vagus nerve stimulation (VNS) and deep brain stimulation (DBS) are the only FDA approved device based therapies. Neither therapy offers complete seizure freedom in a majority of users. Novel methodologies are needed to better understand mechanisms and chronic nature of epilepsy. Most tools for neuromodulation in rodents are tethered. The few wireless devices use batteries or are inductively powered. The tether restricts movement, limits behavioral tests, and increases the risk of infection. Batteries are large and heavy with a limited lifetime. Inductive powering suffers from rapid efficiency drops due to alignment mismatches and increased distances. Miniature wireless tools that offer behavioral freedom, data acquisition, and stimulation are needed. This dissertation presents a platform of electrical, optical and radiofrequency (RF) technologies for device based neuromodulation. The platform can be configured with features including: two channels differential recording, one channel electrical stimulation, and one channel optical stimulation. Typical device operation consumes less than 4 mW. The analog front end has a bandwidth of 0.7 Hz - 1 kHz and a gain of 60 dB, and the constant current driver provides biphasic electrical stimulation. For use with optogenetics, the deep brain optical stimulation module provides 27 mW/mm2 of blue light (473 nm) with 21.01 mA. Pairing of stimulating and recording technologies allows closed-loop operation. A wireless powering cage is designed using the resonantly coupled filter energy transfer (RCFET) methodology. RF energy is coupled through magnetic resonance. The cage has a PTE ranging from 1.8-6.28% for a volume of 11 x 11 x 11 in3. This is sufficient to chronically house subjects. The technologies are validated through various in vivo preparations. The tools are designed to study epilepsy, SUDEP, and urinary incontinence but can be configured for other studies. The broad application of these technologies can enable the scientific community to better study chronic diseases and closed-loop therapies

Controlling and Processing Core for Wireless Implantable Telemetry System

Wireless implantable telemetry systems are suitable choices for monitoring various physiological parameters such as blood pressure and volume. These systems typically compose of an internal device implanted into a living body captures the physiological data and sends them to an external base station located outside of the body for further processing. The internal device usually consists of a sensor interface to convert the collected data to electrical signals; a digital core to digitize the analog signals, process them and prepare them for transmission; an RF front-end to transmit the data outside the body and to receive the required commands from the end station; and a wireless power supply. The digital core plays an important role in these systems since the data must be digitized and processed before transmitting to the end station for further processing. In this thesis, we presented an FPGA-based prototype for controlling and processing core of a miniature implantable telemetry system that is used to monitoring physiological parameters of laboratory small animals. The presented module samples and digitizes the collected data using an analog to digital converter, stores the collected data, generates the controlling output commands, processing the received data, and controls the power consumption of the system. The circuit is prototyped and experimentally verified using an FPGA development platform, then synthesized and simulated in 130 nm CMOS IC technology using standard digital cells. The overall core design occupies 1.6 mm × 1.6 mm CMOS area, and consumes 14.5 mW (IC) or 208 mW (FPGA) total power

Design and Development of Smart Brain-Machine-Brain Interface (SBMIBI) for Deep Brain Stimulation and Other Biomedical Applications

Machine collaboration with the biological body/brain by sending electrical information back and forth is one of the leading research areas in neuro-engineering during the twenty-first century. Hence, Brain-Machine-Brain Interface (BMBI) is a powerful tool for achieving such machine-brain/body collaboration. BMBI generally is a smart device (usually invasive) that can record, store, and analyze neural activities, and generate corresponding responses in the form of electrical pulses to stimulate specific brain regions. The Smart Brain-Machine-Brain-Interface (SBMBI) is a step forward with compared to the traditional BMBI by including smart functions, such as in-electrode local computing capabilities, and availability of cloud connectivity in the system to take the advantage of powerful cloud computation in decision making. In this dissertation work, we designed and developed an innovative form of Smart Brain-Machine-Brain Interface (SBMBI) and studied its feasibility in different biomedical applications. With respect to power management, the SBMBI is a semi-passive platform. The communication module is fully passive—powered by RF harvested energy; whereas, the signal processing core is battery-assisted. The efficiency of the implemented RF energy harvester was measured to be 0.005%. One of potential applications of SBMBI is to configure a Smart Deep-Brain-Stimulator (SDBS) based on the general SBMBI platform. The SDBS consists of brain-implantable smart electrodes and a wireless-connected external controller. The SDBS electrodes operate as completely autonomous electronic implants that are capable of sensing and recording neural activities in real time, performing local processing, and generating arbitrary waveforms for neuro-stimulation. A bidirectional, secure, fully-passive wireless communication backbone was designed and integrated into this smart electrode to maintain contact between the smart electrodes and the controller. The standard EPC-Global protocol has been modified and adopted as the communication protocol in this design. The proposed SDBS, by using a SBMBI platform, was demonstrated and tested through a hardware prototype. Additionally the SBMBI was employed to develop a low-power wireless ECG data acquisition device. This device captures cardiac pulses through a non-invasive magnetic resonance electrode, processes the signal and sends it to the backend computer through the SBMBI interface. Analysis was performed to verify the integrity of received ECG data

PROGRAMMABLE NEURAL PROCESSING FRAMEWORK FOR IMPLANTABLE WIRELESS BRAIN-COMPUTER INTERFACES

Brain-computer interfaces (BCIs) are able to translate cerebral cortex neural activity into control signals for computer cursors or prosthetic limbs. Such neural prosthetics offer tremendous potential for improving the quality of life for disabled individuals. Despite the success of laboratory-based neural prosthetic systems, there is a long way to go before it makes a clinically viable device. The major obstacles include lack of portability due to large physical footprint and performance-power inefficiency of current BCI platforms. Thus, there are growing interests in integrating more BCI's components into a tiny implantable unit, which can minimize the surgical risk and maximize the usability. To date, real-time neural prosthetic systems in laboratory require a wired connection penetrating the skull to a bulky external power/processing unit. For the wireless implantable BCI devices, only the data acquisition and spike detection stages are fully integrated. The rest digital post-processing can only be performed on one chosen channel via custom ASICs, whose lack of flexibility and long development cycle are likely to slow down the ongoing clinical research.This thesis proposes and tests the feasibility of performing on-chip, real-time spike sorting/neural decoding on a programmable wireless sensor network (WSN) node, which is chosen as a compact, low-power platform representative of a future implantable chip. The final accuracy is comparable to state-of-the-art open-loop neural decoder. A detailed power/performance trade-off analysis is presented. Our experimental results show that: 1)direct on-chip neural decoding without spike sorting can achieve 30Hz updating rate, with power density lower than 62mW/cm2; 2)the execution time and power density meet the requirements to perform real-time spike sorting and wireless transmission on a single neural channel. For the option of having spike sorting in order to keep all neural information, we propose a new neural processing workflow that incorporates a light-weight neuron selection method to the training process to reduce the number of channels required for processing. Experimental results show that the proposed method not only narrows the gap between the system requirement and current hardware technology, but also increase the accuracy of the neural decoder by 3%-22%, due to elimination of noisy channels

Design and Evaluation of a Hardware System for Online Signal Processing within Mobile Brain-Computer Interfaces

Brain-Computer Interfaces (BCIs) sind innovative Systeme, die eine direkte Kommunikation zwischen dem Gehirn und externen Geräten ermöglichen. Diese Schnittstellen haben sich zu einer transformativen Lösung nicht nur für Menschen mit neurologischen Verletzungen entwickelt, sondern auch für ein breiteres Spektrum von Menschen, das sowohl medizinische als auch nicht-medizinische Anwendungen umfasst. In der Vergangenheit hat die Herausforderung, dass neurologische Verletzungen nach einer anfänglichen Erholungsphase statisch bleiben, die Forscher dazu veranlasst, innovative Wege zu beschreiten. Seit den 1970er Jahren stehen BCIs an vorderster Front dieser Bemühungen. Mit den Fortschritten in der Forschung haben sich die BCI-Anwendungen erweitert und zeigen ein großes Potenzial für eine Vielzahl von Anwendungen, auch für weniger stark eingeschränkte (zum Beispiel im Kontext von Hörelektronik) sowie völlig gesunde Menschen (zum Beispiel in der Unterhaltungsindustrie). Die Zukunft der BCI-Forschung hängt jedoch auch von der Verfügbarkeit zuverlässiger BCI-Hardware ab, die den Einsatz in der realen Welt gewährleistet. Das im Rahmen dieser Arbeit konzipierte und implementierte CereBridge-System stellt einen bedeutenden Fortschritt in der Brain-Computer-Interface-Technologie dar, da es die gesamte Hardware zur Erfassung und Verarbeitung von EEG-Signalen in ein mobiles System integriert. Die Architektur der Verarbeitungshardware basiert auf einem FPGA mit einem ARM Cortex-M3 innerhalb eines heterogenen ICs, was Flexibilität und Effizienz bei der EEG-Signalverarbeitung gewährleistet. Der modulare Aufbau des Systems, bestehend aus drei einzelnen Boards, gewährleistet die Anpassbarkeit an unterschiedliche Anforderungen. Das komplette System wird an der Kopfhaut befestigt, kann autonom arbeiten, benötigt keine externe Interaktion und wiegt einschließlich der 16-Kanal-EEG-Sensoren nur ca. 56 g. Der Fokus liegt auf voller Mobilität. Das vorgeschlagene anpassbare Datenflusskonzept erleichtert die Untersuchung und nahtlose Integration von Algorithmen und erhöht die Flexibilität des Systems. Dies wird auch durch die Möglichkeit unterstrichen, verschiedene Algorithmen auf EEG-Daten anzuwenden, um unterschiedliche Anwendungsziele zu erreichen. High-Level Synthesis (HLS) wurde verwendet, um die Algorithmen auf das FPGA zu portieren, was den Algorithmenentwicklungsprozess beschleunigt und eine schnelle Implementierung von Algorithmusvarianten ermöglicht. Evaluierungen haben gezeigt, dass das CereBridge-System in der Lage ist, die gesamte Signalverarbeitungskette zu integrieren, die für verschiedene BCI-Anwendungen erforderlich ist. Darüber hinaus kann es mit einer Batterie von mehr als 31 Stunden Dauerbetrieb betrieben werden, was es zu einer praktikablen Lösung für mobile Langzeit-EEG-Aufzeichnungen und reale BCI-Studien macht. Im Vergleich zu bestehenden Forschungsplattformen bietet das CereBridge-System eine bisher unerreichte Leistungsfähigkeit und Ausstattung für ein mobiles BCI. Es erfüllt nicht nur die relevanten Anforderungen an ein mobiles BCI-System, sondern ebnet auch den Weg für eine schnelle Übertragung von Algorithmen aus dem Labor in reale Anwendungen. Im Wesentlichen liefert diese Arbeit einen umfassenden Entwurf für die Entwicklung und Implementierung eines hochmodernen mobilen EEG-basierten BCI-Systems und setzt damit einen neuen Standard für BCI-Hardware, die in der Praxis eingesetzt werden kann.Brain-Computer Interfaces (BCIs) are innovative systems that enable direct communication between the brain and external devices. These interfaces have emerged as a transformative solution not only for individuals with neurological injuries, but also for a broader range of individuals, encompassing both medical and non-medical applications. Historically, the challenge of neurological injury being static after an initial recovery phase has driven researchers to explore innovative avenues. Since the 1970s, BCIs have been at one forefront of these efforts. As research has progressed, BCI applications have expanded, showing potential in a wide range of applications, including those for less severely disabled (e.g. in the context of hearing aids) and completely healthy individuals (e.g. entertainment industry). However, the future of BCI research also depends on the availability of reliable BCI hardware to ensure real-world application. The CereBridge system designed and implemented in this work represents a significant leap forward in brain-computer interface technology by integrating all EEG signal acquisition and processing hardware into a mobile system. The processing hardware architecture is centered around an FPGA with an ARM Cortex-M3 within a heterogeneous IC, ensuring flexibility and efficiency in EEG signal processing. The modular design of the system, consisting of three individual boards, ensures adaptability to different requirements. With a focus on full mobility, the complete system is mounted on the scalp, can operate autonomously, requires no external interaction, and weighs approximately 56g, including 16 channel EEG sensors. The proposed customizable dataflow concept facilitates the exploration and seamless integration of algorithms, increasing the flexibility of the system. This is further underscored by the ability to apply different algorithms to recorded EEG data to meet different application goals. High-Level Synthesis (HLS) was used to port algorithms to the FPGA, accelerating the algorithm development process and facilitating rapid implementation of algorithm variants. Evaluations have shown that the CereBridge system is capable of integrating the complete signal processing chain required for various BCI applications. Furthermore, it can operate continuously for more than 31 hours with a 1800mAh battery, making it a viable solution for long-term mobile EEG recording and real-world BCI studies. Compared to existing research platforms, the CereBridge system offers unprecedented performance and features for a mobile BCI. It not only meets the relevant requirements for a mobile BCI system, but also paves the way for the rapid transition of algorithms from the laboratory to real-world applications. In essence, this work provides a comprehensive blueprint for the development and implementation of a state-of-the-art mobile EEG-based BCI system, setting a new benchmark in BCI hardware for real-world applicability