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Comput Biol Med. 2015 May;60:32-9. doi: 10.1016/j.compbiomed.2015.02.010. Epub 2015 Feb 24.

Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification.

Author information

1
State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi, China. Electronic address: lvna2009@mail.xjtu.edu.cn.
2
State Key Laboratory for Manufacturing Systems Engineering, Systems Engineering Institute, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
3
Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY, USA. Electronic address: Hongyu_miao@urmc.rochetser.edu.

Abstract

BACKGROUND:

Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio.

NEW METHOD:

In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification.

RESULTS:

Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method.

COMPARISON WITH EXISTING METHODS:

Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF.

CONCLUSIONS:

The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint.

KEYWORDS:

Brain computer interface; EEG; Event-related potential; Motor imagery classification; Semi-nonnegative matrix factorization; Structure constraint

[Indexed for MEDLINE]

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