Publications by authors named "Thomas Eggers"

Brain stimulation holds promise for treating brain disorders, but personalizing therapy remains challenging. Effective treatment requires establishing a functional link between stimulation parameters and brain response, yet traditional methods like random sampling (RS) are inefficient and costly. To overcome this, we developed an active learning (AL) framework that identifies optimal relationships between stimulation parameters and brain response with fewer experiments.

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Brain stimulation holds promise for treating brain disorders, but personalizing therapy remains challenging. Effective treatment requires establishing a functional link between stimulation parameters and brain response, yet traditional methods like random sampling (RS) are inefficient and costly. To overcome this, we developed an active learning (AL) framework that identifies optimal relationships between stimulation parameters and brain response with fewer experiments.

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To treat neurological and psychiatric diseases with deep brain stimulation (DBS), a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects.

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. Therapeutic brain stimulation is conventionally delivered using constant-frequency stimulation pulses. Several recent clinical studies have explored how unconventional and irregular temporal stimulation patterns could enable better therapy.

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. The primary challenge faced in the field of neural rehabilitation engineering is the limited advancement in nerve interface technology, which currently fails to match the mechanical properties of small-diameter nerve fascicles. Novel developments are necessary to enable long-term, chronic recording from a multitude of small fascicles, allowing for the recovery of motor intent and sensory signals.

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Electrical nerve block offers the ability to immediately and reversibly block peripheral nerve conduction and would have applications in the emerging field of bioelectronics. Two modalities of electrical nerve block have been investigated-kilohertz frequency alternating current (KHFAC) and direct current (DC). KHFAC can be safely delivered with conventional electrodes, but has the disadvantage of having an onset response, which is a period of increased neural activation before block is established and currently limits clinical translation.

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Background: Kilohertz frequency alternating currents (KHFAC) produce rapid nerve conduction block of mammalian peripheral nerve and have potential clinical applications in reducing peripheral nerve hyperactivity. The experimental investigation of KHFAC nerve block requires a robust output measure and this has proven to be the block threshold (BT), the lowest current or voltage at which the axons of interest are completely blocked. All significant literature in KHFAC nerve block, both simulations and experimental, were reviewed to determine the block threshold method that was used.

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Interfaces with the peripheral nerve provide the ability to extract motor activation and restore sensation to amputee patients. The ability to chronically extract motor activations from the peripheral nervous system remains an unsolved problem. In this study, chronic recordings with the Flat Interface Nerve Electrode (FINE) are employed to recover the activation levels of innervated muscles.

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The ability to reliably and safely communicate chronically with small diameter (100-300 µm) autonomic nerves could have a significant impact in fundamental biomedical research and clinical applications. However, this ability has remained elusive with existing neural interface technologies. Here we show a new chronic nerve interface using highly flexible materials with axon-like dimensions.

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Objective: Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques.

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Objective: Peripheral nerves carry neural signals that could be used to control hybrid bionic systems. Cuff electrodes provide a robust and stable interface but the recorded signal amplitude is small (<3 μVrms 700 Hz-7 kHz), thereby requiring a baseline noise of less than 1 μVrms for a useful signal-to-noise ratio (SNR). Flat interface nerve electrode (FINE) contacts alone generate thermal noise of at least 0.

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People with advanced dementia disease (ADD) are known to have communication difficulties and thus it presents a challenge in understanding the expressions of these people. Because successful communication presupposes cooperation at least between 2 individuals, both individual's actions must be acknowledged. The aim of this study is to describe nursing staff's ways of understanding the expressions of people with ADD when communicating with them.

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Symptoms such as amnesia, agnosia, apraxia, and aphasia may lead to a fragmented experience and actions among people with moderate and severe dementia. The aim of this study was to explore the interactions where fragmentation occurred and how caregivers counteract fragmentation. The observation notes from participant observations were analyzed using interpretive content analysis.

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Human lymphocytes were exposed to the leukemogenic pesticide isofenphos (IFP) to investigate its effects on chromosomal DNA and cholinergic homeostasis using cholinesterase activity as a marker. Isolated peripheral lymphocytes were administered concentrations of IFP ranging from 0.1 ng/ml to 10 microg/ml.

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