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.

Original 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

ASJC Scopus subject areas

  • Biomedical Engineering
  • Mechanical Engineering
  • Artificial Intelligence

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