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

Computers in Biology and Medicine
Na LuHongyu Miao

Abstract

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. 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. 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 experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF...Continue Reading

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Citations

Jun 3, 2016·Computers in Biology and Medicine·Jing LuoNa Lu
Aug 20, 2016·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·Na LuHongyu Miao
Sep 19, 2019·Physiological Measurement·Wei HanHaonan Huang
Sep 7, 2017·Computational Intelligence and Neuroscience·Rensong LiuZixuan Meng
May 14, 2020·Sensors·Juan De La Torre CruzJulio José Carabias Orti
Nov 28, 2020·Journal of Neural Engineering·Foroogh ShamsiLaleh Najafizadeh

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