Study of phase relationships in ECoG signals using Hilbert-Huang transforms

Gahangir Hossain, Mark Myers, Robert Kozma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study investigates phase relationships between electrocorticogram (ECoG) signals through Hilbert-Huang Transform (HHT), combined with Empirical Mode Decomposition (EMD). We perform spatial and temporal filtering of the raw signals, followed by tuning the EMD parameters. It can be seen that carefully tuning of EMD filter, it is possible to capture distinct features of non-stationary data. This makes EMD, combined with HHT a valuable tool of complex brain signal analysis and modeling.

Original languageEnglish (US)
Title of host publicationAdvances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings
Pages174-182
Number of pages9
DOIs
StatePublished - Aug 27 2012
Externally publishedYes
Event5th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012 - Shenyang, China
Duration: Jul 11 2012Jul 14 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7366 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012
CountryChina
CityShenyang
Period7/11/127/14/12

Fingerprint

Hilbert-Huang Transform
Decomposition
Decompose
Tuning
Mathematical transformations
Signal Analysis
Signal analysis
Brain
Filtering
Filter
Distinct
Relationships
Modeling

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hossain, G., Myers, M., & Kozma, R. (2012). Study of phase relationships in ECoG signals using Hilbert-Huang transforms. In Advances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings (pp. 174-182). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7366 LNAI). https://doi.org/10.1007/978-3-642-31561-9_19

Study of phase relationships in ECoG signals using Hilbert-Huang transforms. / Hossain, Gahangir; Myers, Mark; Kozma, Robert.

Advances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings. 2012. p. 174-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7366 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hossain, G, Myers, M & Kozma, R 2012, Study of phase relationships in ECoG signals using Hilbert-Huang transforms. in Advances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7366 LNAI, pp. 174-182, 5th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012, Shenyang, China, 7/11/12. https://doi.org/10.1007/978-3-642-31561-9_19
Hossain G, Myers M, Kozma R. Study of phase relationships in ECoG signals using Hilbert-Huang transforms. In Advances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings. 2012. p. 174-182. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-31561-9_19
Hossain, Gahangir ; Myers, Mark ; Kozma, Robert. / Study of phase relationships in ECoG signals using Hilbert-Huang transforms. Advances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings. 2012. pp. 174-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{4e57b293965c499daeca1b621777ce59,
title = "Study of phase relationships in ECoG signals using Hilbert-Huang transforms",
abstract = "This study investigates phase relationships between electrocorticogram (ECoG) signals through Hilbert-Huang Transform (HHT), combined with Empirical Mode Decomposition (EMD). We perform spatial and temporal filtering of the raw signals, followed by tuning the EMD parameters. It can be seen that carefully tuning of EMD filter, it is possible to capture distinct features of non-stationary data. This makes EMD, combined with HHT a valuable tool of complex brain signal analysis and modeling.",
author = "Gahangir Hossain and Mark Myers and Robert Kozma",
year = "2012",
month = "8",
day = "27",
doi = "10.1007/978-3-642-31561-9_19",
language = "English (US)",
isbn = "9783642315602",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "174--182",
booktitle = "Advances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings",

}

TY - GEN

T1 - Study of phase relationships in ECoG signals using Hilbert-Huang transforms

AU - Hossain, Gahangir

AU - Myers, Mark

AU - Kozma, Robert

PY - 2012/8/27

Y1 - 2012/8/27

N2 - This study investigates phase relationships between electrocorticogram (ECoG) signals through Hilbert-Huang Transform (HHT), combined with Empirical Mode Decomposition (EMD). We perform spatial and temporal filtering of the raw signals, followed by tuning the EMD parameters. It can be seen that carefully tuning of EMD filter, it is possible to capture distinct features of non-stationary data. This makes EMD, combined with HHT a valuable tool of complex brain signal analysis and modeling.

AB - This study investigates phase relationships between electrocorticogram (ECoG) signals through Hilbert-Huang Transform (HHT), combined with Empirical Mode Decomposition (EMD). We perform spatial and temporal filtering of the raw signals, followed by tuning the EMD parameters. It can be seen that carefully tuning of EMD filter, it is possible to capture distinct features of non-stationary data. This makes EMD, combined with HHT a valuable tool of complex brain signal analysis and modeling.

UR - http://www.scopus.com/inward/record.url?scp=84865214411&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84865214411&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-31561-9_19

DO - 10.1007/978-3-642-31561-9_19

M3 - Conference contribution

SN - 9783642315602

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

SP - 174

EP - 182

BT - Advances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings

ER -