Fall compensation detection from EEG using neuroevolution and genetic hyperparameter optimisation

Bird, J.J. ORCID: 0000-0002-9858-1231 and Lotfi, A. ORCID: 0000-0002-5139-6565, 2023. Fall compensation detection from EEG using neuroevolution and genetic hyperparameter optimisation. Genetic Programming and Evolvable Machines, 24 (1): 6. ISSN 1389-2576

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Detecting fall compensatory behaviour from large EEG datasets poses a difficult problem in big data which can be alleviated by evolutionary computation-based machine learning strategies. In this article, hyperheuristic optimisation solutions via evolutionary optimisation of deep neural network topologies and genetic programming of machine learning pipelines will be investigated. Wavelet extractions from signals recorded during physical activities present a binary problem for detecting fall compensation. The earlier results show that a Gaussian process model achieves an accuracy of 86.48%. Following this, artificial neural networks are evolved through evolutionary algorithms and score similarly to most standard models; the hyperparameters chosen are well outside the bounds of batch or manual searches. Five iterations of genetic programming scored higher than all other approaches, at a mean 90.52% accuracy. The best pipeline extracted polynomial features and performed Principal Components Analysis, before machine learning through a randomised set of decision trees, and passing the class prediction probabilities to a 72-nearest-neighbour algorithm. The best genetic solution could infer data in 0.02 s, whereas the second best genetic programming solution (89.79%) could infer data in only 0.3 ms.

Item Type: Journal article
Publication Title: Genetic Programming and Evolvable Machines
Creators: Bird, J.J. and Lotfi, A.
Publisher: Springer Science and Business Media LLC
Date: 2023
Volume: 24
Number: 1
ISSN: 1389-2576
Rights: © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 18 May 2023 15:27
Last Modified: 18 May 2023 15:27
URI: https://irep.ntu.ac.uk/id/eprint/49012

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