Intra-subject Invariant Classification Modeling for Spectral Features in EEG Signals Using Decision Fusion Method

Sunghee Dong, Jichai Jeong

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Intra-subject variability of the oscillatory activity in EEG signals limits the personal-adaptability of brain-computer interfaces for neurorehabilitation. The main object of this paper is to construct a fused classification model which is robust to the individual differences in the optimal frequency bands for classifying the spectral features into the dual or single tasks. The proposed decision fusion model results in the higher classification accuracy of 6%, compared to the averaged test accuracy of single classifiers using the best performing band as spectral features. Our study expands the usage of EEG spectral features for neuro-rehabilitation systems without selecting a specific frequency range depending on subject, task or environment.

LanguageEnglish
Title of host publicationBiosystems and Biorobotics
PublisherSpringer International Publishing
Pages1126-1130
Number of pages5
DOIs
Publication statusPublished - 2019 Jan 1

Publication series

NameBiosystems and Biorobotics
Volume21
ISSN (Print)2195-3562
ISSN (Electronic)2195-3570

Fingerprint

Electroencephalography
Fusion reactions
Brain computer interface
Patient rehabilitation
Frequency bands
Classifiers

ASJC Scopus subject areas

  • Biomedical Engineering
  • Mechanical Engineering
  • Artificial Intelligence

Cite this

Dong, S., & Jeong, J. (2019). Intra-subject Invariant Classification Modeling for Spectral Features in EEG Signals Using Decision Fusion Method. In Biosystems and Biorobotics (pp. 1126-1130). (Biosystems and Biorobotics; Vol. 21). Springer International Publishing. https://doi.org/10.1007/978-3-030-01845-0_225

Intra-subject Invariant Classification Modeling for Spectral Features in EEG Signals Using Decision Fusion Method. / Dong, Sunghee; Jeong, Jichai.

Biosystems and Biorobotics. Springer International Publishing, 2019. p. 1126-1130 (Biosystems and Biorobotics; Vol. 21).

Research output: Chapter in Book/Report/Conference proceedingChapter

Dong, S & Jeong, J 2019, Intra-subject Invariant Classification Modeling for Spectral Features in EEG Signals Using Decision Fusion Method. in Biosystems and Biorobotics. Biosystems and Biorobotics, vol. 21, Springer International Publishing, pp. 1126-1130. https://doi.org/10.1007/978-3-030-01845-0_225
Dong S, Jeong J. Intra-subject Invariant Classification Modeling for Spectral Features in EEG Signals Using Decision Fusion Method. In Biosystems and Biorobotics. Springer International Publishing. 2019. p. 1126-1130. (Biosystems and Biorobotics). https://doi.org/10.1007/978-3-030-01845-0_225
Dong, Sunghee ; Jeong, Jichai. / Intra-subject Invariant Classification Modeling for Spectral Features in EEG Signals Using Decision Fusion Method. Biosystems and Biorobotics. Springer International Publishing, 2019. pp. 1126-1130 (Biosystems and Biorobotics).
@inbook{cf78667a7ea7459b894b78107e0d9d0c,
title = "Intra-subject Invariant Classification Modeling for Spectral Features in EEG Signals Using Decision Fusion Method",
abstract = "Intra-subject variability of the oscillatory activity in EEG signals limits the personal-adaptability of brain-computer interfaces for neurorehabilitation. The main object of this paper is to construct a fused classification model which is robust to the individual differences in the optimal frequency bands for classifying the spectral features into the dual or single tasks. The proposed decision fusion model results in the higher classification accuracy of 6{\%}, compared to the averaged test accuracy of single classifiers using the best performing band as spectral features. Our study expands the usage of EEG spectral features for neuro-rehabilitation systems without selecting a specific frequency range depending on subject, task or environment.",
author = "Sunghee Dong and Jichai Jeong",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-3-030-01845-0_225",
language = "English",
series = "Biosystems and Biorobotics",
publisher = "Springer International Publishing",
pages = "1126--1130",
booktitle = "Biosystems and Biorobotics",

}

TY - CHAP

T1 - Intra-subject Invariant Classification Modeling for Spectral Features in EEG Signals Using Decision Fusion Method

AU - Dong, Sunghee

AU - Jeong, Jichai

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Intra-subject variability of the oscillatory activity in EEG signals limits the personal-adaptability of brain-computer interfaces for neurorehabilitation. The main object of this paper is to construct a fused classification model which is robust to the individual differences in the optimal frequency bands for classifying the spectral features into the dual or single tasks. The proposed decision fusion model results in the higher classification accuracy of 6%, compared to the averaged test accuracy of single classifiers using the best performing band as spectral features. Our study expands the usage of EEG spectral features for neuro-rehabilitation systems without selecting a specific frequency range depending on subject, task or environment.

AB - Intra-subject variability of the oscillatory activity in EEG signals limits the personal-adaptability of brain-computer interfaces for neurorehabilitation. The main object of this paper is to construct a fused classification model which is robust to the individual differences in the optimal frequency bands for classifying the spectral features into the dual or single tasks. The proposed decision fusion model results in the higher classification accuracy of 6%, compared to the averaged test accuracy of single classifiers using the best performing band as spectral features. Our study expands the usage of EEG spectral features for neuro-rehabilitation systems without selecting a specific frequency range depending on subject, task or environment.

UR - http://www.scopus.com/inward/record.url?scp=85055316718&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85055316718&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-01845-0_225

DO - 10.1007/978-3-030-01845-0_225

M3 - Chapter

T3 - Biosystems and Biorobotics

SP - 1126

EP - 1130

BT - Biosystems and Biorobotics

PB - Springer International Publishing

ER -