When the nerve cell of human is damaged, the central neural system (CNS) can not work properly. Instead of sending commands by CNS, we can use a micro-stimulation method to send commands to hands, legs, or organs. The first part of this thesis presents a fully implantable system for neural micro-stimulation and neural signal monitoring, and introduces the communication protocol and baseband circuitry of the system. Due to the rapidly development of small and portable electrical equipments, low power becomes more and more important because of the limitation of the battery capacity. Meanwhile, the embedded memory in these devices consumes considerable power. In this thesis, we present a low-power sequential memory decoder to resolve the power-dissipation of embedded memories. We¡¦ll verify that the sequential decoder can reduce the power consumption compared to traditional decoders by implementing our idea with a 2-Kbit SRAM memory.

Simultaneous recordings of single and multi-unit neural signals from multiple cortical areas in the brain are a vital tool for gaining more understanding of the operating mechanism of the brain as well as for developing Brain Machine Interfaces. Monitoring the activity levels of hundreds or even thousands of neurons can lead to reliable decoding of brain signals for controlling prosthesis of multiple degrees of freedom and different functionalities. With the advancement of high density microelectrode arrays, the craving of neuroscience research to record the activity of thousands of neurons is achievable. Recently CMOS-based Micro-electrode Arrays MEAs featuring high spatial and temporal resolution have been reported. The augmentation in the number of recording sites carries different challenges to the neural signal processing system. The primary challenge is the massive increase in the incoming data that needs to be transmitted and processed in real time. Data reduction based on the sparse nature of the neural signals with respect to time becomes essential. The dissertation presents the design of a neural spike-based data reduction platform that can handle a few thousands of channels on Field Programmable Gate Arrays (FPGAs), making use of their massive parallel processing capabilities and reconfigurability. For Standalone implementation the spike detector core uses Finite State Machines (FSMs) to control the interface with the data acquisition as well as sending the spike waveforms to a common output FIFO. The designed neural signal processing platform integrates the application of high-speed serial Multi-Gigabit transceivers on FPGAs to allow massive data transmission in real time. It also provides a design for autonomous threshold setting for each channel. / Electrical and Computer Engineering

Abstract The goal of this thesis was to investigate and build new circuit solutions for electrophysiological measurements that would be used in biophysical research of nervous system and brain activity. The first aim was to build a micromotion compensation system that could compensate for the relative movement of measurement microelectrodes and neurons that can cause signal attenuation or even loss. The purpose of this work was to stabilize the microelectrode with respect to the preparation in order to achieve more stable measurements with small test animals, such as insects, rodents or reptiles. The movement is measured with a touch probe sensor and a feedback loop containing a piezoelectric actuator that adjusts the position of the electrode. A prototype micromotion compensation system was built and its performance was measured in a realistic measurement condition. The compensation system was used to reduce the motion of the probe to below 1 µm, resulting in up to 98% compensation below 10 Hz. The design of the micromotion compensation system took advantage of a preceding study on a piezoelectric bimorph actuator/sensor structure. This study is also presented in the thesis. Another aim of the research was to design and build an integrated multichannel neural signal recording system with stimulation capabilities. The circuit was designed to amplify, digitize and stream out data from extracellular neuronal signal measurements. The main target of the measurement system are action potential signals, which are a type of “digital communication” between nerve cells that evolution has produced. The waveform of these action potential signals is the focus of interest. To accomplish this measurement, the developed circuit contains preamplification, multiplexing, post-amplification, A/D conversion and control logic for the A/D converter and data transmission. The circuit is also externally programmable, and it contains DACs for tuning high-pass filter corner frequency, amplifier bias current and stimulation current. The implemented electronics have low noise, low power and small circuit area. The gain of the circuit is adjustable from 100 to 5000 and the high-pass filter corner frequency from 0.5 Hz to 900 Hz. The sample rate is 20.833 kSps and the data rate is 3.5 Mbps. The measured noise level of the circuit is 7.5μV (rms) (300 Hz - 10 kHz) and the whole chip consumes less than 2 mW of power. A 16-channel prototype chip with 0.35μm CMOS technology was manufactured and its performance was measured. Backend electronics containing a microcontroller supporting high-speed USB data transfer was also programmed for the system. The device was tested in real measurements of neuronal signals in a cockroach (Periplaneta americana) preparation, and reliable streaming of the recorded data to the PC verified its proper function. / Tiivistelmä Tämän väitöskirjatyön tavoitteena oli kehittää mittaus- ja säätöjärjestelmiä aivotutkimuksen ja biofysiikan sovelluksiin. Ensimmäisenä tutkimuskokonaisuutena oli mittaus- ja säätöjärjestelmän kehittäminen, minkä tavoitteena oli mahdollistaa aivojen sähköisen signaloinnin mittaaminen mahdollisimman luonnollisessa tilassa olevilla koe-eläimillä (esim. hyönteiset, matelijat tai pienet nisäkkäät). Tätä varten kehitettiin aktiivinen liikekompensointimekanismi, jossa kosketusanturilla mitattiin aivokudoksen mikrometriluokan mekaanista liikettä ja kompensoitiin sähköistä mittausta suorittavan anturin ja aivon välinen suhteellinen liike liikuttamalla takaisinkytkentälenkissä olevaa pietsosähköistä aktuaattoria. Kompensointimekanismin toiminta testattiin realistisissa mittausolosuhteissa. Liikekompensoinnilla saatiin vähennettyä mittausanturin liikettä suhteessa kudokseen alle mikrometriin, maksimikompensoinnin ollessa noin 98 % alle 10 Hz:n taajuudella. Väitöskirjaan liitettiin pietsosähköisiin komponentteihin liittyen taustatiedoksi artikkeli aiemmin suunnitellusta pietsosähköisestä bimorph aktuaattori/sensori -komponentista. Toisen tutkimuskokonaisuuden muodosti suurten hermosolupopulaatioiden toiminnan mittaamiseen sekä stimulointiin kehitetty monikanavainen järjestelmä. Tärkeimpänä mittauskohteena työssä ovat ekstrasellulaariset aktiopotentiaalisignaalit, jotka ovat eräänlainen evoluution tuottama “digitaalinen” hermosolujen välinen kommunikaatiomenetelmä. Kiinnostuksen kohteena ovat näiden aktiopotentiaalisignaalien aaltomuodot. Mittauksia varten työssä kehitettiin hermosolujen solun ulkopuoliseen nesteeseen asetettaviin elektrodeihin kytkettävä elektroniikka, jolla pystytään sekä stimuloimaan että mittaamaan jokaista elektrodia. Suunniteltu vahvistinelektroniikka on matalakohinainen, matalatehoinen ja pienikokoinen. Mittausjärjestelmään on suunniteltu myös multipleksointi, A/D-muunninelektroniikka sekä ohjauslogiikka, joka sisältää muunnostulosten puskuroinnin integroidun piirin rekisteripankkeihin, SPI-liitynnän high-speed USB protokollaa tukevalle mikrokontrollerille sekä konfiguraatiorekistereitä, joihin SPI-väylän kautta kirjoittamalla voidaan säätää piirin vahvistusta, operaatiovahvistimien biasvirtoja, kaistanleveyttä sekä stimulaatiovirtojen voimakkuuksia. Piirin vahvistus on säädettävissä 100:n ja 5000:n välillä ja ylipäästösuodatuksen kulmataajuus välillä 0,5 Hz - 900 Hz. Piirin näytteistystaajuus on 20,833 kSps ja tiedonsiirtonopeus 3,5 Mbps. Piirin kohinatasoksi mitattiin 7,5 µV (rms) (300 Hz - 10 kHz) ja koko piirin tehonkulutukseksi alle 2 mW. Integroidusta piiristä valmistettiin 16-kanavainen prototyyppi 0,35 µm:n CMOS-teknologialla. Kehitetyn laitteen toiminta varmistettiin mittaamalla hermosignaaleja torakkapreparaatista (Periplaneta americana). Mittausdata siirrettiin onnistuneesti ja luotettavasti PC:lle.

Brain Computer Interfaces (BCI) are devices that translate acquired neural signals to command and control signals. Applications of BCI include neural rehabilitation and neural prosthesis (thought controlled wheelchair, thought controlled speller etc.) to aid patients with disabilities and to augment human computer interaction. A successful practical BCI requires a faithful acquisition modality to record high quality neural signals; a signal processing system to construct appropriate features from these signals; and an algorithm to translate these features to appropriate outputs. Intracortical recordings like local field potentials provide reliable high SNR signals over long periods and suit BCI applications well. However, the non-stationarity of neural signals poses a challenge in robust decoding of subject behavior. Most BCI research focuses either on developing daily re-calibrated decoders that require exhaustive training sessions; or on providing cross-validation results. Such results ignore the variation of signal characteristics over different sessions and provide an optimistic estimate of BCI performance. Specifically, traditional BCI algorithms fail to perform at the same level on chronological data recordings. Neural signals are susceptible to variations in signal characteristics due to changes in subject behavior and learning, and variability in electrode characteristics due to tissue interactions. While training day-specific BCI overcomes signal variability, BCI re-training causes user frustration and exhaustion. This dissertation presents contributions to solve these challenges in BCI research. Specifically, we developed decoders trained on a single recording session and applied them on subsequently recorded sessions. This strategy evaluates BCI in a practical scenario with a potential to alleviate BCI user frustration without compromising performance. The initial part of the dissertation investigates extracting features that remain robust to changes in neural signal over several days of recordings. It presents a qualitative feature extraction technique based on ranking the instantaneous power of multichannel data. These qualitative features remain robust to outliers and changes in the baseline of neural recordings, while extracting discriminative information. These features form the foundation in developing robust decoders. Next, this dissertation presents a novel algorithm based on the hypothesis that multiple neural spatial patterns describe the variation in behavior. The presented algorithm outperforms the traditional methods in decoding over chronological recordings. Adapting such a decoder over multiple recording sessions (over 6 weeks) provided > 90% accuracy in decoding eight movement directions. In comparison, performance of traditional algorithms like Common Spatial Patterns deteriorates to 16% over the same time. Over time, adaptation reinforces some spatial patterns while diminishing others. Characterizing these spatial patterns reduces model complexity without user input, while retaining the same accuracy levels. Lastly, this dissertation provides an algorithm that overcomes the variation in recording quality. Chronic electrode implantation causes changes in signal-to-noise ratio (SNR) of neural signals. Thus, some signals and their corresponding features available during training become unavailable during testing and vice-versa. The proposed algorithm uses prior knowledge on spatial pattern evolution to estimate unknown neural features. This algorithm overcomes SNR variations and provides up to 93% decoding of eight movement directions over 6 weeks. Since model training requires only one session, this strategy reduces user frustration. In a practical closed-loop BCI, the user learns to produce stable spatial patterns, which improves performance of the proposed algorithms. / text

This thesis focuses on the recording challenges faced in biomedical systems. More specifically, the challenges in neural signal recording are explored. Instead of the typical synchronous ADC system, a level crossing ADC is detailed as it has gained recent interest for low-power biomedical systems. These systems take advantage of the time-sparse nature of the signals found in this application. A 10-bit design is presented to help capture the lower amplitude action potentials (APs) in neural signals. The design also achieves a full-scale bandwidth of 1.2 kHz, an ENOB of 9.81, a power consumption of 13.5 microwatts, operating at a supply voltage of 1.8 V. This design was simulated in Cadence using 180 nm CMOS technology.

No description available.

No / An area-efficient hardware architecture is used to map fully parallel cortical columns on Field Programmable Gate Arrays (FPGA) is presented in this paper. To demonstrate the concept of this work, the proposed architecture is shown at the system level and benchmarked with image and speech recognition applications. Due to the spatio-temporal nature of spiking neurons, this has allowed such architectures to map on FPGAs in which communication can be performed through the use of spikes and signal can be represented in binary form. The process and viability of designing and implementing the multiple recurrent neural reservoirs with a novel multiplier-less reconfigurable architectures is described.

The key requirements of a reliable neural signal recording system include low power to support long-term monitoring, low noise, minimum tissue damage, and wireless transmission. The neural spikes are also detected and sorted on-chip/off-chip to implement closed-loop neuromodulation in a high channel count setup. All these features together constitute an empirical neural recording system for neuroscience research. In this prospectus, we propose to develop a neural signal acquisition system with wireless transmission and feature extraction. We start by designing a prototype entirely built with commercial-off-the-shelf components, which includes recording and wireless transmission of synthetic neural data and feature extraction. We then conduct the CMOS implementation of the low-power multi-channel neural signal recording read-out circuit, which enables the in-vivo recording with a small form factor. Another direction of this thesis is to design a self-powered motion tracking read-out circuit for wearable sensors. As the wearable industry continues to advance, the need for self-powered medical devices is growing significantly. In this line of research, we propose a self-powered motion sensor based on reverse electrowetting-on-dielectric (REWOD) with low-power integrated electronics for remotely monitoring health conditions. We design the low-power read-out circuit for a wide range of input charges, which is generated from the REWOD sensor.

Decoding algorithms for brain machine interfaces map neural firing times to the underlying biological output signal through dynamic tuning functions. In order to maintain an accurate estimate of the biological signal, the state of the tuning function parameters must be tracked simultaneously. The evolution of this system state is often estimated by an adaptive filter. Recent work demonstrates that the Bayesian auxiliary particle filter (BAPF) offers improved estimates of the system state and underlying output signal over existing techniques. Performance of the BAPF is evaluated under both ideal conditions and commonly encountered spike detection errors such as missed and false detections and missorted spikes. However, this increase in neuronal signal decoding accuracy is at the expense of an increase in computational complexity. Real-time execution of the BAPF algorithm for neural signals using a sequential processor becomes prohibitive as the number of particles and neurons in the obs / Electrical and Computer Engineering

No description available.