Modulation of neural activity through electrical stimulation of tissue is an effective therapy for neurological diseases such as Parkinson's disease and essential tremor. Researchers are exploring improving therapy through adjustment of stimulation parameters based upon sensed data. This requires classifiers to extract features and estimate patient state. It also requires algorithms to appropriately map the state estimation to stimulation parameters. The latter, known as the control policy algorithm, is the focus of this work. Because the optimal control policy algorithms for the nervous system are not fully characterized at this time, we have implemented a generic control policy framework to facilitate exploratory research and rapid prototyping of new neuromodulation strategies.

Chronically implantable, closed-loop neuromodulation devices with concurrent sensing and stimulation hold promise for better understanding the nervous system and improving therapies for neurological disease. Concurrent sensing and stimulation are needed to maximize usable neural data, minimize time delays for closed-loop actuation, and investigate the instantaneous response to stimulation. Current systems lack concurrent sensing and stimulation primarily because of stimulation interference to neural signals of interest. While careful design of high performance amplifiers has proved useful to reduce disturbances in the system, stimulation continues to contaminate neural sensing due to biological effects like tissue-electrode impedance mismatch and constraints on stimulation parameters needed to deliver therapy. In this work we describe systematic methods to mitigate the effect of stimulation through a combination of sensing hardware, stimulation parameter selection, and classification algorithms that counter residual stimulation disturbances. To validate these methods we implemented and tested a completely implantable system for over one year in a large animal model of epilepsy. The system proved capable of measuring and detecting seizure activity in the hippocampus both during and after stimulation. Furthermore, we demonstrate an embedded algorithm that actuates neural modulation in response to seizure detection during stimulation, validating the capability to detect bioelectrical markers in the presence of therapy and titrate it appropriately. The capability to detect neural states in the presence of stimulation and optimally titrate therapy is a key innovation required for generalizing closed-loop neural systems for multiple disease states.

We briefly describe a dynamic control system framework for neuromodulation for epilepsy, with an emphasis on its practical challenges and the preliminary validation of key prototype technologies in a chronic animal model. The current state of neuromodulation can be viewed as a classical dynamic control framework such that the nervous system is the classical "plant", the neural stimulator is the controller/actuator, clinical observation, patient diaries and/or measured bio-markers are the sensor, and clinical judgment applied to these sensor inputs forms the state estimator. Technology can potentially address two main factors contributing to the performance limitations of existing systems: "observability," the ability to observe the state of the system from output measurements, and "controllability," the ability to drive the system to a desired state. In addition to improving sensors and actuator performance, methods and tools to better understand disease state dynamics and state estimation are also critical for improving therapy outcomes. We describe our preliminary validation of key "observability" and "controllability" technology blocks using an implanted research tool in an epilepsy disease model. This model allows for testing the key emerging technologies in a representative neural network of therapeutic importance. In the future, we believe these technologies might enable both first principles understanding of neural network behavior for optimizing therapy design, and provide a practical pathway towards clinical translation.

Implantable neurostimulators for the treatment of epilepsy that are capable of sensing seizures can enable novel therapeutic applications. However, detecting seizures is challenging due to significant intracranial EEG signal variability across patients. In this paper, we illustrate how a machine-learning based, patient-specific seizure detector provides better performance and lower power consumption than a patient non-specific detector using the same seizure library. The machine-learning based architecture was fully implemented in the micropower domain, demonstrating feasibility for an embedded detector in implantable systems.

An implantable bi-directional brain-machine interface (BMI) prototype is presented. With sensing, algorithm, wireless telemetry, and stimulation therapy capabilities, the system is designed for chronic studies exploring closed-loop and diagnostic opportunities for neuroprosthetics. In particular, we hope to enable fundamental chronic research into the physiology of neurological disorders, define key electrical biomarkers related to disease, and apply this learning to patient-specific algorithms for therapeutic stimulation and diagnostics. The ultimate goal is to provide practical neuroprosthetics with adaptive therapy for improved efficiency and efficacy.

The monitoring of neuronal activity could potentially expand the diagnostic and therapeutic capabilities of neuroprosthesis. The challenge of designing sensing and control systems is two-fold: first, the signal input must be robust for chronic recording; second, the circuit architecture must be capable of achieving signal processing, algorithm control, and telemetry with a limited power budget. The first requirement should be met by measuring field potentials, which represent ensemble behavior in a neural network and can be measured chronically. For the second requirement, architecting an effective solution requires identification of the key information of interest and partitioning the signal chain to play to the strengths of analog vs. digital processing. For many neurological states of interest, information 'biomarkers' are encoded as low frequency power fluctuations within well-defined frequency bands of field potentials, similar to the amplitude modulation found in an AM radio. Recognizing this similarity, the feasibility prototype adapts a chopper-stabilized instrumentation amplifier to act as a superheterodyning AM receiver for brain signals. Since the physiological power fluctuations are generally orders of magnitude slower than the frequency at which they are encoded, the use of efficient analog preprocessing greatly reduces the overall energy requirements for implementing a complete mixed- signal system. Since the science of field potentials is rapidly evolving, the superheterodyning chopper is advantageous given its flexibility and immunity to process, temperature, and mismatch variations. This paper will discuss the design of a complete system prototype for a neurostimulator research tool; the design has a noise floor of under 2muVrms and a total system current of 25muW/processing channel (1.8V supply) while performing biomarker extraction, algorithmic processing and control, and data loop recording.

This paper describes a prototype architecture that tracks the power fluctuations in discrete frequency bands for a broad spectrum of neuronal biomarkers. The circuit merges chopper-stabilization with heterodyne signal processing to construct a low-noise amplifier with highly programmable, robust filtering characteristics. It is concluded that in our application, the area and modest noise penalty are offset by the benefit of a well-partitioned analog-to-digital boundary flexible enough to potentially extract a wide spectrum of biomarkers associated with disease.

This paper describes an amplification and spectral processing IC for extracting key bicielectrical signals, or "biomarkers", which are expressed in the brain's field potentials. The intent is to explore using these biomarkers to drive prosthetic actuators or titrate therapy devices such as a deep-brain neurostimulator. The prototype IC uses 5 mu W/channel to resolve signals on the order of 1 mu V-rms. The four channels on the device provide independent spectral analysis from DC to 1 kHz, with variable bandwidth and power filtering characteristics. The noise floor and. flexible spectral processing support a broad range of potential applications including sleep staging, Parkinson's disease, detection of movement intention for neuroprosthesis, and detection of high frequency "fast ripples" for exploring seizure prediction. To fully demonstrate the IC's functionality, we include results from a prototype "closed-loop" neurostimulator implementing adaptive titration of therapy based on measured field potential activity.