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

Sunghee Dong; Jichai Jeong

doi:10.1007/978-3-030-01845-0_225

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 proceeding › Chapter

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.

Original language	English
Title of host publication	Biosystems and Biorobotics
Publisher	Springer International Publishing
Pages	1126-1130
Number of pages	5
DOIs	https://doi.org/10.1007/978-3-030-01845-0_225
Publication status	Published - 2019

Publication series

Name	Biosystems and Biorobotics
Volume	21
ISSN (Print)	2195-3562
ISSN (Electronic)	2195-3570

Bibliographical note

Funding Information:
This work was supported in part by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451) and by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology under Grant NRF-2018R1D1A1B07042378.

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

ASJC Scopus subject areas

Biomedical Engineering
Mechanical Engineering
Artificial Intelligence

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

Cite this

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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",

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

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