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

Conference paper
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Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 21)

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.

Keywords

Neuro-rehabilitation System Decision Fusion Model Optimal Frequency Band Brain Computer Interface (BCI) Intra-subject Variability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
  2. 2.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea

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