A HIGHLY-SCALABLE DC-COUPLED DIRECT-ADC NEURAL RECORDING CHANNEL ARCHITECTURE WITH INPUT-ADAPTIVE RESOLUTION

This thesis presents the design, development, and characterization of a novel neural recording channel architecture with (a) quantization resolution that is adaptive to the input signal's level of activity, (b) fully-dynamic power consumption that is linearly proportional to the recording resolution, and (c) immunity to DC offset and drifts at the input. Our results demonstrate the proposed design's capability in conducting neural recording with near lossless input-adaptive data compression, leading to a significant reduction in the energy required for both recording and data transmission, hence allowing for a potential high scaling of the number of recording channels integrated on a single implanted microchip without the need to increase the power budget. The proposed channel with the implemented compression technique is implemented in a standard 130nm CMOS technology with overall power consumption of 7.6uW and active area of 92×92µm for the implemented digital-backend

A HIGHLY-SCALABLE DC-COUPLED DIRECT-ADC NEURAL RECORDING CHANNEL ARCHITECTURE WITH INPUT-ADAPTIVE RESOLUTION

This thesis presents the design, development, and characterization of a novel neural recording channel architecture with (a) quantization resolution that is adaptive to the input signal's level of activity, (b) fully-dynamic power consumption that is linearly proportional to the recording resolution, and (c) immunity to DC offset and drifts at the input. Our results demonstrate the proposed design's capability in conducting neural recording with near lossless input-adaptive data compression, leading to a significant reduction in the energy required for both recording and data transmission, hence allowing for a potential high scaling of the number of recording channels integrated on a single implanted microchip without the need to increase the power budget. The proposed channel with the implemented compression technique is implemented in a standard 130nm CMOS technology with overall power consumption of 7.6uW and active area of 9292m for the implemented digital-backend

Area and Power Efficient Ultra-Wideband Transmitter Based on Active Inductor

This paper presents the design of an impulse radio ultra-wideband (IR-UWB) transmitter for low-power, short-range, and high-data rate applications such as high density neural recording interfaces. The IR-UWB transmitter pulses are generated by modulating the output of a local oscillator. The large area requirement of the spiral inductor in a conventional on-chip LC tank is overcome by replacing it with an active inductor topology. The circuit has been fabricated in a UMC CMOS 180 nm technology, with a die area of 0.012 mm2. The temporal width of the output waveform is determined by a pulse generator based on logic gates. The measured pulse is compliant with Federal Communications Commission (FCC) power spectral density limits and within the frequency band of 3-6 GHz. For the minimum pulse duration of 1 ns, the energy consumption of the design is 20 pJ per bit, while transmitting at a 200 Mbps data rate with an amplitude of 130 mV

An Energy-Efficient Spiking CNN Implementation for Cross-Patient Epileptic Seizure Detection

This research aims to develop a data-driven computationally efficient strategy for automatic cross-patient seizure detection using spatio temporal features learned from multichannel electroencephalogram (EEG) time-series data. In this approach, we utilize an algorithm that seeks to capture spectral, temporal, and spatial information in order to achieve high generalization. This algorithm's initial step is to convert EEG signals into a series of temporal and multi-spectral pictures. The produced images are then sent into a convolutional neural network (CNN) as inputs. Our convolutional neural network as a deep learning method learns a general spatially irreducible representation of a seizure to improves sensitivity, specificity, and accuracy results comparable to the state-of-the-art results. In this work, in order to avoid the inherent high computational cost of CNNs while benefiting from their superior classification performance, a neuromorphic computing strategy for seizure prediction called spiking CNN is developed from the traditional CNN method, which is motivated by the energy-efficient spiking neural networks (SNNs) of the human brain

Computationally efficient algorithms and implementations of adaptive deep brain stimulation systems for Parkinson's disease

Clinical deep brain stimulation (DBS) is a tool used to mitigate pharmacologically intractable neurodegenerative diseases such as Parkinson's disease (PD), tremor and dystonia. Present implementations of DBS use continuous, high frequency voltage or current pulses so as to mitigate PD. This results in some limitations, among which there is stimulation induced side effects and shortening of pacemaker battery life. Adaptive DBS (aDBS) can be used to overcome a number of these limitations. Adaptive DBS is intended to deliver stimulation precisely only when needed. This thesis presents work undertaken to investigate, propose and develop novel algorithms and implementations of systems for adapting DBS. This thesis proposes four system implementations that could facilitate DBS adaptation either in the form of closed-loop DBS or spatial adaptation. The first method involved the use of dynamic detection to track changes in local field potentials (LFP) which can be indicative of PD symptoms. The work on dynamic detection included the synthesis of validation dataset using mainly autoregressive moving average (ARMA) models to enable the evaluation of a subset of PD detection algorithms for accuracy and complexity trade-offs. The subset of algorithms consisted of feature extraction (FE), dimensionality reduction (DR) and dynamic pattern classification stages. The combination with the best trade-off in terms of accuracy and complexity consisted of discrete wavelet transform (DWT) for FE, maximum ratio method (MRM) for DR and k-nearest neighbours (k-NN) for classification. The MRM is a novel DR method inspired by Fisher's separability criterion. The best combination achieved accuracy measures: F1-score of 97.9%, choice probability of 99.86% and classification accuracy of 99.29%. Regarding complexity, it had an estimated microchip area of 0.84 mm² for estimates in 90 nm CMOS process. The second implementation developed the first known PD detection and monitoring processor. This was achieved using complementary detection, which presents a hardware-efficient method of implementing a PD detection processor for monitoring PD progression in Parkinsonian patients. Complementary detection is achieved by using a combination of weak classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers in the configuration. The PD detection processor using the same processing stages as the first implementation was validated on an FPGA platform. By mapping the implemented design on a 45 nm CMOS process, the most optimal implementation achieved a dynamic power per channel of 2.26 μW and an area per channel of 0.2384 mm². It also achieved mean accuracy measures: Mathews correlation coefficient (MCC) of 0.6162, an F1-score of 91.38%, and mean classification accuracy of 91.91%. The third implementation proposed a framework for adapting DBS based on a critic-actor control approach. This models the relationship between a trained clinician (critic) and a neuro-modulation system (actor) for modulating DBS. The critic was implemented and validated using machine learning models, and the actor was implemented using a fuzzy controller. Therapy is modulated based on state estimates obtained through the machine learning models. PD suppression was achieved in seven out of nine test cases. The final implementation introduces spatial adaptation for aDBS. Spatial adaptation adjusts to variation in lead position and/or stimulation focus, as poor stimulation focus has been reported to affect therapeutic benefits of DBS. The implementation proposes dynamic current steering systems as a power-efficient implementation for multi-polar multisite current steering, with a particular focus on the output stage of the dynamic current steering system. The output stage uses dynamic current sources in implementing push-pull current sources that are interfaced to 16 electrodes so as to enable current steering. The performance of the output stage was demonstrated using a supply of 3.3 V to drive biphasic current pulses of up to 0.5 mA through its electrodes. The preliminary design of the circuit was implemented in 0.18 μm CMOS technology

Inductively Coupled CMOS Power Receiver For Embedded Microsensors

Inductively coupled power transfer can extend the lifetime of embedded microsensors that save costs, energy, and lives. To expand the microsensors' functionality, the transferred power needs to be maximized. Plus, the power receiver needs to handle wide coupling variations in real applications. Therefore, the objective of this research is to design a power receiver that outputs the highest power for the widest coupling range. This research proposes a switched resonant half-bridge power stage that adjusts both energy transfer frequency and duration so the output power is maximally high. A maximum power point (MPP) theory is also developed to predict the optimal settings of the power stage with 98.6% accuracy. Finally, this research addresses the system integration challenges such as synchronization and over-voltage protection. The fabricated self-synchronized prototype outputs up to 89% of the available power across 0.067%~7.9% coupling range. The output power (in percentage of available power) and coupling range are 1.3× and 13× higher than the comparable state of the arts.Ph.D

A Study of Techniques and Mechanisms of Vagus Nerve Stimulation for Treatment of Inflammation

Vagus nerve stimulation (VNS) has been on the forefront of inflammatory disorder research for the better part of the last three decades and has yielded many promising results. There remains, however, much debate about the actual biological mechanisms of such treatments, as well as, questions about inconsistencies in methods used in many research efforts. In this work, I identify shortcomings in past VNS methods and submit new developments and findings that can progress the research community towards more selective and relevant VNS research and treatments. In Aim 1, I present the most recent advancements in the capabilities of our fully implantable Bionode stimulation device platform for use in VNS studies to include stimulation circuitry, device packaging, and stimulation cuff design. In Aim 2, I characterize the inflammatory cytokine response of rats to intraperitoneally injected endotoxin utilizing new data analysis methods and demonstrate the modulatory effects of VNS applied by the Bionode stimulator to subdiaphragmatic branches of the left vagus nerve in an acute study. In Aim 3, using fully implanted Bionode devices, I expose a previously unidentified effect of chronically cuffing the left cervical vagus nerve to suppress efferent Fluorogold transport and cause unintended attenuation to physiological effects of VNS. Finally, in accordance with our findings from Aims 1, 2, and 3, I present results from new and promising techniques we have explored for future use of VNS in inflammation studies