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.
| Language | English |
|---|---|
| Title of host publication | Biosystems and Biorobotics |
| Publisher | Springer International Publishing |
| Pages | 1126-1130 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
Publication series
| Name | Biosystems and Biorobotics |
|---|---|
| Volume | 21 |
| ISSN (Print) | 2195-3562 |
| ISSN (Electronic) | 2195-3570 |
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ASJC Scopus subject areas
- Biomedical Engineering
- Mechanical Engineering
- Artificial Intelligence
Cite this
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 proceeding › Chapter
}
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.
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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 -