Browsing by Author "Fan, Yingying"

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    Integrated microheater array for efficient and localized heating of magnetic nanoparticles at microwave frequencies
    (2025-02-11) Fan, Yingying; Zhang, Qingbo; Zhang, Linlin; Bao, Gang; Chi, Taiyun; Rice University; United States Patent and Trademark Office
    An microheater array system includes an integrated microheater array configured to generate a localized heat and having a plurality of pixels. Each pixel includes: an inductor; a stacked oscillator configured to generate a magnetic field at microwave frequencies with tunable intensity and frequency; and an electro-thermal loop. The microheater array system may further include a plurality of magnetic nanoparticles (MNPs).
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    Integrated Microheater Array for Localized Heating of Magnetic Nanoparticles
    (2022-12-01) Fan, Yingying; Chi, Taiyun
    There is a growing interest in leveraging microwave technology for biomedical applications including non-invasive imaging, respiratory and heartbeat radar detection, and spectroscopy. While most of these applications are sensing-oriented, my research focuses on microwave actuation, particularly heat generation. It enables a novel non-invasive approach for applications such as tumor ablation and neural stimulations. A variety of techniques have been employed to raise the local temperature for these emerging biomedical applications including dielectric heating and ohmic heating. However, they suffer from poor material specificity and undesired damage to the surrounding tissues. Our key idea is to explore magnetic heating induced by magnetic nanoparticles (MNP) which offers superior heating specificity. We proposed and demonstrated a miniaturized microheater array utilizing ferromagnetic resonance of the MNP at GHz to enable localized yet accurate temperature manipulation in living cells and tissues.
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    Interfacing the brain: high-channel-count neural recording and minimally invasive brain stimulation ASICs
    (2024-12-03) Fan, Yingying; Chi, Taiyun
    The brain's complexity governs our interactions with the world, and unraveling its mysteries could transform the diagnosis and treatment of neurological disorders, which pose a significant health challenge. Specialized tools, particularly neural interfaces, are crucial in this pursuit. These interfaces act as communication pathways between the brain and external devices. My research addresses two critical areas: high-channel-count neural recording and minimally invasive neural stimulation. In the realm of neural recording, current technologies face challenges in scalability, limiting the number of neurons that can be recorded simultaneously. This limitation hinders our ability to fully understand the brain's complex communication networks. My work focuses on developing advanced recording systems capable of capturing the activity of a larger number of neurons concurrently. On the stimulation side, traditional electrical methods raise concerns about long-term safety due to the electrode-tissue interface. While non-invasive techniques such as Transcranial Magnetic Stimulation (TMS) offer an alternative, they suffer from limitations in precision and hardware bulkiness. My research aims to develop minimally invasive stimulation techniques that mitigate these issues, offering safer and more precise methods to modulate brain activity. By addressing these two critical challenges, my work strives to push the boundaries of neural interfacing, bringing us closer to a deeper understanding of brain function and its potential therapeutic applications.
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    Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries
    (IOP Publishing, 2021) Schmid, William; Fan, Yingying; Chi, Taiyun; Golanov, Eugene; Regnier-Golanov, Angelique S.; Austerman, Ryan J.; Podell, Kenneth; Cherukuri, Paul; Bentley, Timothy; Steele, Christopher T.; Schodrof, Sarah; Aazhang, Behnaam; Britz, Gavin W.; Neuroengineering Initiative (NEI)
    Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making ‘go/no-go’ decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute and early-stage mTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.