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Classification of intended motor movement using surface EEG ensemble empirical mode decomposition.

Authors
  • Kuo, Ching-Chang
  • Lin, William S
  • Dressel, Chelsea A
  • Chiu, Alan W L
Type
Published Article
Journal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publication Date
Jan 01, 2011
Volume
2011
Pages
6281–6284
Identifiers
DOI: 10.1109/IEMBS.2011.6091550
PMID: 22255774
Source
Medline
License
Unknown

Abstract

Noninvasive electroencephalography (EEG) brain computer interface (BCI) systems are used to investigate intended arm reaching tasks. The main goal of the work is to create a device with a control scheme that allows those with limited motor control to have more command over potential prosthetic devices. Four healthy subjects were recruited to perform various reaching tasks directed by visual cues. Independent component analysis (ICA) was used to identify artifacts. Active post parietal cortex (PPC) activation before arm movement was validated using EEGLAB. Single-trial binary classification strategies using support vector machine (SVM) with radial basis functions (RBF) kernels and Fisher linear discrimination (FLD) were evaluated using signal features from surface electrodes near the PPC regions. No significant improvement can be found by using a nonlinear SVM over a linear FLD classifier (63.65% to 63.41% accuracy). A significant improvement in classification accuracy was found when a normalization factor based on visual cue "signature" was introduced to the raw signal (90.43%) and the intrinsic mode functions (IMF) of the data (93.55%) using Ensemble Empirical Mode Decomposition (EEMD).

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