Improving understanding of EEG measurements using transparent machine learning models

Chris Roadknight, Guanyu Zong, Prapa Rattadilok

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationHealth Information Science - 8th International Conference, HIS 2019, Proceedings
EditorsHua Wang, Siuly Siuly, Yanchun Zhang, Rui Zhou, Fernando Martin-Sanchez, Zhisheng Huang
PublisherSpringer
Pages134-142
Number of pages9
ISBN (Print)9783030329617
DOIs
Publication statusPublished - 2019
Event8th International Conference on Health Information Science, HIS 2019 - Xi'an, China
Duration: 18 Oct 201920 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11837 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Health Information Science, HIS 2019
Country/TerritoryChina
CityXi'an
Period18/10/1920/10/19

Keywords

  • CAPing
  • Deep Learning
  • Physiological data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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