Improving understanding of EEG measurements using transparent machine learning models

Roadknight, Chris and Zong, Guanyu and Rattadilok, Prapa (2019) Improving understanding of EEG measurements using transparent machine learning models. In: Health Information Science. Lecture Notes in Computer Science book series, 11837 . Springer Nature, Switzerland, pp. 134-142. ISBN 9783030329617

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Abstract

Physiological datasets such as Electroencephalography (EEG) data offer an insight into some of the less well understood aspects of human physiology. This paper investigates simple methods to develop models of high level behavior from low level electrode readings. These methods include using neuron activity based pruning and large time slices of the data. Both approaches lead to solutions whose performance and transparency are superior to existing methods.

Item Type: Book Section
Keywords: Deep Learning; Physiological data; CAPing
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number: https://doi.org/10.1007/978-3-030-32962-4_13
Depositing User: Yu, Tiffany
Date Deposited: 09 Jan 2020 03:58
Last Modified: 09 Jan 2020 03:59
URI: http://eprints.nottingham.ac.uk/id/eprint/59612

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