Improving understanding of EEG measurements using transparent machine learning models

Chris Roadknight; Guanyu Zong; Prapa Rattadilok

doi:10.1007/978-3-030-32962-4_13

Improving understanding of EEG measurements using transparent machine learning models

Chris Roadknight, Guanyu Zong, Prapa Rattadilok

School of Computer Science

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

20 Downloads (Pure)

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 language	English
Title of host publication	Health Information Science - 8th International Conference, HIS 2019, Proceedings
Editors	Hua Wang, Siuly Siuly, Yanchun Zhang, Rui Zhou, Fernando Martin-Sanchez, Zhisheng Huang
Publisher	Springer
Pages	134-142
Number of pages	9
ISBN (Print)	9783030329617
DOIs	https://doi.org/10.1007/978-3-030-32962-4_13
Publication status	Published - 2019
Event	8th International Conference on Health Information Science, HIS 2019 - Xi'an, China Duration: 18 Oct 2019 → 20 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11837 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Health Information Science, HIS 2019
Country/Territory	China
City	Xi'an
Period	18/10/19 → 20/10/19

Keywords

CAPing
Deep Learning
Physiological data

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-32962-4_13

203 ImprovingAccepted author manuscript, 826 KBLicence: CC BY

Cite this

Roadknight, C., Zong, G., & Rattadilok, P. (2019). Improving understanding of EEG measurements using transparent machine learning models. In H. Wang, S. Siuly, Y. Zhang, R. Zhou, F. Martin-Sanchez, & Z. Huang (Eds.), Health Information Science - 8th International Conference, HIS 2019, Proceedings (pp. 134-142). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11837 LNCS). Springer. https://doi.org/10.1007/978-3-030-32962-4_13

Roadknight, Chris ; Zong, Guanyu ; Rattadilok, Prapa. / Improving understanding of EEG measurements using transparent machine learning models. Health Information Science - 8th International Conference, HIS 2019, Proceedings. editor / Hua Wang ; Siuly Siuly ; Yanchun Zhang ; Rui Zhou ; Fernando Martin-Sanchez ; Zhisheng Huang. Springer, 2019. pp. 134-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5e935b9a57e34d1b8b1b3d61a239afde,

title = "Improving understanding of EEG measurements using transparent machine learning models",

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.",

keywords = "CAPing, Deep Learning, Physiological data",

author = "Chris Roadknight and Guanyu Zong and Prapa Rattadilok",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 8th International Conference on Health Information Science, HIS 2019 ; Conference date: 18-10-2019 Through 20-10-2019",

year = "2019",

doi = "10.1007/978-3-030-32962-4_13",

language = "English",

isbn = "9783030329617",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "134--142",

editor = "Hua Wang and Siuly Siuly and Yanchun Zhang and Rui Zhou and Fernando Martin-Sanchez and Zhisheng Huang",

booktitle = "Health Information Science - 8th International Conference, HIS 2019, Proceedings",

}

Roadknight, C, Zong, G & Rattadilok, P 2019, Improving understanding of EEG measurements using transparent machine learning models. in H Wang, S Siuly, Y Zhang, R Zhou, F Martin-Sanchez & Z Huang (eds), Health Information Science - 8th International Conference, HIS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11837 LNCS, Springer, pp. 134-142, 8th International Conference on Health Information Science, HIS 2019, Xi'an, China, 18/10/19. https://doi.org/10.1007/978-3-030-32962-4_13

Improving understanding of EEG measurements using transparent machine learning models. / Roadknight, Chris; Zong, Guanyu; Rattadilok, Prapa.
Health Information Science - 8th International Conference, HIS 2019, Proceedings. ed. / Hua Wang; Siuly Siuly; Yanchun Zhang; Rui Zhou; Fernando Martin-Sanchez; Zhisheng Huang. Springer, 2019. p. 134-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11837 LNCS).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Improving understanding of EEG measurements using transparent machine learning models

AU - Roadknight, Chris

AU - Zong, Guanyu

AU - Rattadilok, Prapa

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - CAPing

KW - Deep Learning

KW - Physiological data

UR - http://www.scopus.com/inward/record.url?scp=85075662589&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-32962-4_13

DO - 10.1007/978-3-030-32962-4_13

M3 - Conference contribution

AN - SCOPUS:85075662589

SN - 9783030329617

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 134

EP - 142

BT - Health Information Science - 8th International Conference, HIS 2019, Proceedings

A2 - Wang, Hua

A2 - Siuly, Siuly

A2 - Zhang, Yanchun

A2 - Zhou, Rui

A2 - Martin-Sanchez, Fernando

A2 - Huang, Zhisheng

PB - Springer

T2 - 8th International Conference on Health Information Science, HIS 2019

Y2 - 18 October 2019 through 20 October 2019

ER -

Roadknight C, Zong G, Rattadilok P. Improving understanding of EEG measurements using transparent machine learning models. In Wang H, Siuly S, Zhang Y, Zhou R, Martin-Sanchez F, Huang Z, editors, Health Information Science - 8th International Conference, HIS 2019, Proceedings. Springer. 2019. p. 134-142. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32962-4_13

Improving understanding of EEG measurements using transparent machine learning models

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this