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20182026
most citedPath Signatures for Seizure Forecasting

2 citations · 4 across the 13 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

q-bio.NC2023

Epileptic seizure forecasting with long short-term memory (LSTM) neural networks

Daniel E. Payne, Jordan D. Chambers, Anthony Burkitt +4

Objective: Forecasting epileptic seizures can reduce uncertainty for patients and allow preventative actions. While many models can predict the occurrence of seizures from features…

stat.ML20232 cited

Path Signatures for Seizure Forecasting

Jonas F. Haderlein, Andre D. H. Peterson, Parvin Zarei Eskikand +4

Predicting future system behaviour from past observed behaviour (time series) is fundamental to science and engineering. In computational neuroscience, the prediction of future epi…

eess.SP20231 cited

Comparison of Sub-Scalp EEG and Endovascular Stent-Electrode Array for Visual Evoked Potential Brain-Computer Interface

Timothy B. Mahoney, Po-Chen Liu, David B Grayden +1

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 th…

q-bio.NC2023

Understanding visual processing of motion: Completing the picture using experimentally driven computational models of MT

Parvin Zarei Eskikand, David B Grayden, Tatiana Kameneva +2

Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by wh…

eess.SP2023

Autoregressive models for biomedical signal processing

Jonas F. Haderlein, Andre D. H. Peterson, Anthony N. Burkitt +2

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,…

math.OC2023

On the benefit of overparameterisation in state reconstruction: An empirical study of the nonlinear case

Jonas F. Haderlein, Andre D. H. Peterson, Parvin Zarei Eskikand +3

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…