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Parkinson's disease (PD) is a progressive, neurodegenerative disorder, characterized by hallmark motor symptoms. Deep brain stimulation (DBS) has been used to treat advanced PD successfully. Previous studies have found that the DBS also has an effect on the electrophysiological activity of the deep brain nucleus while alleviate the PD symptoms. Here, in an attempt to gain a greater understanding of...
Quantitative assessment of the motor symptoms in Parkinson's disease is the key to early treatment and essential for long term management over years. Wearable devices provide a new approach to quantify the motor symptoms in Parkinson's disease. Monitoring of the symptoms at multi-joints and body, on-line monitoring, quick analysis, low load and less restriction to the patients raise challenges to...
Parkinson's disease (PD) is a neurodegenerative disorder and severely affects patients' daily lives. The diagnosis depends on the presence of one or more common motor symptoms of the disease. As PD is chronic and slowly progressive, the symptoms continue and worsen over a long period of time. Quantification of the PD's motor symptoms is helpful for diagnosing and monitoring patients' conditions. This...
In the sensory thalamus and periventricular gray/periaqueductal gray (PVAG) nucleus, the synchronization level of multiple frequency band oscillations of local field potentials (LFPs) have been shown to be associated with chronic pain perception and modulation. In this study, a state identification approach was generated to dynamically identify the synchronization state of neural oscillation. In this...
Local field potentials (LFPs) contain rich information of deep brain nucleus. And the dynamic multiple oscillations' synchronization activity of deep brain structures are involved in the neurophysiological and neuropathological function of a nucleus. In this study, a state identification approach was generated for identify the synchronization state of theta (6–9 Hz) and alpha (9–12 Hz) neural oscillations...
The use of Deep Brain Local Field Potentials (LFP) in the process of connecting the human brain with artificial devices is one of the most promising fields in neural engineering. Inner mechanisms of our the central nervous system (CNS) can be understood through the study of LFPs. Of special importance are the the LFPs that come from subthalamic nucleus (STN) as they are related to the preparation,...
With the aging of the population, Parkinson's disease (PD) is increasing and severely threating to human life and health. Quantitative assessment of the symptoms in PD is the key for precise diagnosis and treatment and essential for long term management over years. Currently, the evaluation of the symptoms in PD mainly relies on Unified Parkinson's Disease Rating Scale (UPDRS), which is affected by...
A functioning thalamus is essential for treatment of patients with disorders of consciousness (DOC) using deep brain stimulation (DBS). This work aims to identify the potential biomarkers related to consciousness from the thalamic deep brain local field potentials (LFPs) in DOC patients. The frequency features of central thalamic LFPs were characterized with spectral analysis. The features were further...
The study aimed to develop a miniature and wirelessly controlled optical stimulator for optogenetics neuro modulation in freely moving small animals. The light source was generated from micro-LEDs. The optical stimulation parameters of frequency, light power and pulse width were wirelessly programmed through radio-frequency communication between a computer and the stimulator. The stimulator was evaluated...
Objectives: The inaccurate implantation of deep brain stimulation electrode could cause in-effective therapy and varied side-effects. This study aims to intra-operatively localise the subthalamic nucleus (STN) using voluntary movement related local field potentials (LFPs) to improve the electrode implantation accuracy. Methods: STN LFPs were recorded during auditory cued clicking motor task in twelve...
Nonlinear interaction between neural oscillations may be a biomarker for closed-loop deep brain stimulation (DBS) in Parkinson's disease. Bispectral analysis is an effective tool to detect quadratic phase coupling. In this paper we applied bispectral analysis to local field potentials (LFPs) recorded from Parkinsonism patients' subthalamic nucleus (STN) during tremor and resting states. The significance...
Electromagnetic (EM) energy has been commonly adopted in our daily lives in recent decades; human safety issues of EM waves have continuously been a concern to the public. Look directly into the human cells to define the radiation and emission requirements for human safety, experiments have been conducted, pioneering the biomedical impact of Electromagnetic radiation exposure to cell cultures. This...
Non-negative matrix factorization (NMF) has increasingly been used as a tool in signal processing in the last years, but it has not been used in the cochlear implants (CIs). To improve the performance of CIs in noisy environments, a novel sparse strategy is proposed by applying NMF on envelopes of 22 channels. In the new algorithm, the noisy speech is first transferred to the time-frequency domain...
In this paper we introduce a robust classification framework for tongue-movement ear pressure signals based around an ensemble voting methodology. The ensemble members are comprised of different combinations of sensor inputs i.e. two in-ear microphones and an acoustic gel sensor positioned under the chin of the individual and classification using three different base models. It is shown that by using...
Tongue movement ear pressure (TMEP) signals have been used to generate controlling commands in assistive human machine interfaces aimed at people with disabilities. The objective of this study is to classify the controlled movement related signals of an intended action from internally occurring physiological signals which can interfere with the inter-movement classification. TMEP signals were collected,...
Real-time implementation of an assistive human-machine interface system based around tongue-movement ear pressure (TMEP) signals is presented, alongside results from a series of simulated control tasks. The implementation of this system into an online setting involves short-term energy calculation, detection, segmentation and subsequent signal classification, all of which had to be reformulated based...
Tongue movement ear pressure signals have been used to generate controlling commands in human-machine interfaces. The objective of this study is to classify the controlled movement relating to an intended action from interfering signals that can be experienced. These interfering signals include but are not limited to, speech, coughing and drinking. Thus data was collected for six types of controlled...
The goal of this work was to develop and implement a new filtering strategy to denoise acoustic signals in the ear canal resulting from voluntary movement of the tongue (as a method of generating control input), as well as from other active actions, (speech, eating, drinking, smoking), and passive actions (swallowing, adjusting the jaw, physiological activity). The strategy is based on a denoising...
We aimed to identify neural oscillations in the time-frequency representation of local field potentials recorded from the subthalamic nucleus. The time-frequency representation was normalised over the global mean and standard deviation global normalisation, or against the baseline period at each frequency, local normalisation. The cross-correlation between beta and gamma oscillations was enhanced...
In this work, we present our study on platinum nanoparticle-coated carbon nanotubes for amperometric glucose biosensing. Transmission electron microscopy and X-ray diffraction were used to characterize the nanomaterial. To investigate its potential biosensing application, platinum and carbon nanomaterial-based electrodes were fabricated and characterized electrochemically. Our experimental results...
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