Zobrazeno 1 - 10
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pro vyhledávání: '"Grayden, David"'
Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis
Externí odkaz:
http://arxiv.org/abs/2411.18916
Autor:
Russo, John S., Mahoney, Tim, Kokorin, Kirill, Reynolds, Ashley, Lin, Chin-Hsuan Sophie, John, Sam E., Grayden, David B.
Multiple Sclerosis (MS) is a severely disabling condition that leads to various neurological symptoms. A Brain-Computer Interface (BCI) may substitute some lost function; however, there is a lack of BCI research in people with MS. To progress this re
Externí odkaz:
http://arxiv.org/abs/2404.04965
Autor:
Payne, Daniel E., Chambers, Jordan D., Burkitt, Anthony, Cook, Mark J., Kuhlman, Levin, Freestone, Dean R., Grayden, David B.
Objective: Forecasting epileptic seizures can reduce uncertainty for patients and allow preventative actions. While many models can predict the occurrence of seizures from features of the EEG, few models incorporate changes in features over time. Lon
Externí odkaz:
http://arxiv.org/abs/2309.09471
Autor:
Haderlein, Jonas F., Peterson, Andre D. H., Eskikand, Parvin Zarei, Cook, Mark J., Burkitt, Anthony N., Mareels, Iven M. Y., Grayden, David B.
Predicting future system behaviour from past observed behaviour (time series) is fundamental to science and engineering. In computational neuroscience, the prediction of future epileptic seizures from brain activity measurements, using EEG data, rema
Externí odkaz:
http://arxiv.org/abs/2308.09312
Autor:
Eskikand, Parvin Zarei, Grayden, David B, Kameneva, Tatiana, Burkitt, Anthony N, Ibbotson, Michael R
Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by which we can better understand and predict the principles of neural comp
Externí odkaz:
http://arxiv.org/abs/2305.09317
Brain-computer interfaces (BCI) have the potential to improve the quality of life for persons with paralysis. Sub-scalp EEG provides an alternative BCI signal acquisition method that compromises between the limitations of traditional EEG systems and
Externí odkaz:
http://arxiv.org/abs/2304.13238
Autor:
Haderlein, Jonas F., Peterson, Andre D. H., Eskikand, Parvin Zarei, Burkitt, Anthony N., Mareels, Iven M. Y., Grayden, David B.
The empirical success of machine learning models with many more parameters than measurements has generated an interest in the theory of overparameterisation, i.e., underdetermined models. This paradigm has recently been studied in domains such as dee
Externí odkaz:
http://arxiv.org/abs/2304.08066
Autor:
Haderlein, Jonas F., Peterson, Andre D. H., Burkitt, Anthony N., Mareels, Iven M. Y., Grayden, David B.
Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially
Externí odkaz:
http://arxiv.org/abs/2304.11070
Autor:
Liu, Yueyang, Soto-Breceda, Artemio, Zhao, Yun, Karoly, Phillipa, Cook, Mark J., Grayden, David B., Schmidt, Daniel, Kuhlmann1, Levin
Objective Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to EEG. However, this approach lacks a reliable method to determine the initial filter conditions and assumes that
Externí odkaz:
http://arxiv.org/abs/2301.08391
Autor:
Zarei Eskikand, Parvin, Soto-Breceda, Artemio, Cook, Mark J., Burkitt, Anthony N., Grayden, David B.
Publikováno v:
In Neural Networks December 2024 180
