Matching Items (4)
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- All Subjects: MEMS
- Creators: Buneo, Christopher
- Creators: Christen, Jennifer Blain
Description
Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the desired skill level. It would result in more reliable and adaptive neural interfaces that could record optimal neural activity 24/7 with high fidelity signals, high yield and increased throughput. The main contribution here is validating adaptive strategies to overcome challenges in autonomous navigation of microelectrodes inside the brain. The following issues pose significant challenges as brain tissue is both functionally and structurally dynamic: a) time varying mechanical properties of the brain tissue-microelectrode interface due to the hyperelastic, viscoelastic nature of brain tissue b) non-stationarities in the neural signal caused by mechanical and physiological events in the interface and c) the lack of visual feedback of microelectrode position in brain tissue. A closed loop control algorithm is proposed here for autonomous navigation of microelectrodes in brain tissue while optimizing the signal-to-noise ratio of multi-unit neural recordings. The algorithm incorporates a quantitative understanding of constitutive mechanical properties of soft viscoelastic tissue like the brain and is guided by models that predict stresses developed in brain tissue during movement of the microelectrode. An optimal movement strategy is developed that achieves precise positioning of microelectrodes in the brain by minimizing the stresses developed in the surrounding tissue during navigation and maximizing the speed of movement. Results of testing the closed-loop control paradigm in short-term rodent experiments validated that it was possible to achieve a consistently high quality SNR throughout the duration of the experiment. At the systems level, new generation of MEMS actuators for movable microelectrode array are characterized and the MEMS device operation parameters are optimized for improved performance and reliability. Further, recommendations for packaging to minimize the form factor of the implant; design of device mounting and implantation techniques of MEMS microelectrode array to enhance the longevity of the implant are also included in a top-down approach to achieve a reliable brain interface.
ContributorsAnand, Sindhu (Author) / Muthuswamy, Jitendran (Thesis advisor) / Tillery, Stephen H (Committee member) / Buneo, Christopher (Committee member) / Abbas, James (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
Long-term monitoring of deep brain structures using microelectrode implants is critical for the success of emerging clinical applications including cortical neural prostheses, deep brain stimulation and other neurobiology studies such as progression of disease states, learning and memory, brain mapping etc. However, current microelectrode technologies are not capable enough of reaching those clinical milestones given their inconsistency in performance and reliability in long-term studies. In all the aforementioned applications, it is important to understand the limitations & demands posed by technology as well as biological processes. Recent advances in implantable Micro Electro Mechanical Systems (MEMS) technology have tremendous potential and opens a plethora of opportunities for long term studies which were not possible before. The overall goal of the project is to develop large scale autonomous, movable, micro-scale interfaces which can seek and monitor/stimulate large ensembles of precisely targeted neurons and neuronal networks that can be applied for brain mapping in behaving animals. However, there are serious technical (fabrication) challenges related to packaging and interconnects, examples of which include: lack of current industry standards in chip-scale packaging techniques for silicon chips with movable microstructures, incompatible micro-bonding techniques to elongate current micro-electrode length to reach deep brain structures, inability to achieve hermetic isolation of implantable devices from biological tissue and fluids (i.e. cerebrospinal fluid (CSF), blood, etc.). The specific aims are to: 1) optimize & automate chip scale packaging of MEMS devices with unique requirements not amenable to conventional industry standards with respect to bonding, process temperature and pressure in order to achieve scalability 2) develop a novel micro-bonding technique to extend the length of current polysilicon micro-electrodes to reach and monitor deep brain structures 3) design & develop high throughput packaging mechanism for constructing a dense array of movable microelectrodes. Using a combination of unique micro-bonding technique which involves conductive thermosetting epoxy’s with hermetically sealed support structures and a highly optimized, semi-automated, 90-minute flip-chip packaging process, I have now extended the repertoire of previously reported movable microelectrode arrays to bond conventional stainless steel and Pt/Ir microelectrode arrays of desired lengths to steerable polysilicon shafts. I tested scalable prototypes in rigorous bench top tests including Impedance measurements, accelerated aging and non-destructive testing to assess electrical and mechanical stability of micro-bonds under long-term implantation. I propose a 3D printed packaging method allows a wide variety of electrode configurations to be realized such as a rectangular or circular array configuration or other arbitrary geometries optimal for specific regions of the brain with inter-electrode distance as low as 25 um with an unprecedented capability of seeking and recording/stimulating targeted single neurons in deep brain structures up to 10 mm deep (with 6 μm displacement resolution). The advantage of this computer controlled moveable deep brain electrodes facilitates potential capabilities of moving past glial sheath surrounding microelectrodes to restore neural connection, counter the variabilities in signal amplitudes, and enable simultaneous recording/stimulation at precisely targeted layers of brain.
ContributorsPalaniswamy, Sivakumar (Author) / Muthuswamy, Jitendran (Thesis advisor) / Buneo, Christopher (Committee member) / Abbas, James (Committee member) / Arizona State University (Publisher)
Created2016
Description
Intracranial pressure is an important parameter to monitor, and elevated intracranial pressure can be life threatening. Elevated intracranial pressure is indicative of distress in the brain attributed by conditions such as aneurysm, traumatic brain injury, brain tumor, hydrocephalus, stroke, or meningitis.
Electrocorticography (ECoG) recordings are invaluable in understanding epilepsy and detecting seizure zones. However, ECoG electrodes cause a foreign body mass effect, swelling, and pneumocephaly, which results in elevation of intracranial pressure (ICP). Thus, the aim of this work is to design an intracranial pressure monitoring system that could augment ECoG electrodes.
A minimally invasive, low-cost epidural intracranial pressure monitoring system is developed for this purpose, using a commercial pressure transducer available for biomedical applications. The system is composed of a pressure transducer, sensing cup, electronics, and data acquisition system. The pressure transducer is a microelectromechanical system (MEMS)-based die that works on piezoresistive phenomenon with dielectric isolation for direct contact with fluids.
The developed system was bench tested and verified in an animal model to confirm the efficacy of the system for intracranial pressure monitoring. The system has a 0.1 mmHg accuracy and a 2% error for the 0-10 mmHg range, with resolution of 0.01 mmHg. This system serves as a minimally invasive (2 mm burr hole) epidural ICP monitor, which could augment existing ECoG electrode arrays, to simultaneously measure intracranial pressure along with the neural signals.
This device could also be employed with brain implants that causes elevation in ICP due to tissue - implant interaction often leading to edema. This research explores the concept and feasibility for integrating the sensing component directly on to the ECoG electrode arrays.
Electrocorticography (ECoG) recordings are invaluable in understanding epilepsy and detecting seizure zones. However, ECoG electrodes cause a foreign body mass effect, swelling, and pneumocephaly, which results in elevation of intracranial pressure (ICP). Thus, the aim of this work is to design an intracranial pressure monitoring system that could augment ECoG electrodes.
A minimally invasive, low-cost epidural intracranial pressure monitoring system is developed for this purpose, using a commercial pressure transducer available for biomedical applications. The system is composed of a pressure transducer, sensing cup, electronics, and data acquisition system. The pressure transducer is a microelectromechanical system (MEMS)-based die that works on piezoresistive phenomenon with dielectric isolation for direct contact with fluids.
The developed system was bench tested and verified in an animal model to confirm the efficacy of the system for intracranial pressure monitoring. The system has a 0.1 mmHg accuracy and a 2% error for the 0-10 mmHg range, with resolution of 0.01 mmHg. This system serves as a minimally invasive (2 mm burr hole) epidural ICP monitor, which could augment existing ECoG electrode arrays, to simultaneously measure intracranial pressure along with the neural signals.
This device could also be employed with brain implants that causes elevation in ICP due to tissue - implant interaction often leading to edema. This research explores the concept and feasibility for integrating the sensing component directly on to the ECoG electrode arrays.
ContributorsSampath Kumaran, Ranjani (Author) / Christen, Jennifer Blain (Thesis advisor) / Tillery, Stephen Helms (Committee member) / Greger, Bradley (Committee member) / Arizona State University (Publisher)
Created2015
Description
Micro Electro Mechanical Systems (MEMS) based accelerometers are one of the most commonly used sensors out there. They are used in devices such as, airbags, smartphones, airplanes, and many more. Although they are very accurate, they degrade with time or get offset due to some damage. To fix this, they must be calibrated again using physical calibration technique, which is an expensive process to conduct. However, these sensors can also be calibrated infield by applying an on-chip electrical stimulus to the sensor. Electrical stimulus-based calibration could bring the cost of testing and calibration significantly down as compared to factory testing. In this thesis, simulations are presented to formulate a statistical prediction model based on an electrical stimulus. Results from two different approaches of electrical calibration have been discussed. A prediction model with a root mean square error of 1% has been presented in this work. Experiments were conducted on commercially available accelerometers to test the techniques used for simulations.
ContributorsBassi, Ishaan (Author) / Ozev, Sule (Thesis advisor) / Christen, Jennifer Blain (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2020