Nonnegative tensor factorization for continuous EEG classification.
ABSTRACT In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.
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ABSTRACT: Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-series analysis, spectral analysis and matrix decomposition. Indeed, EEG signals are often naturally born with more than two modes of time and space, and they can be denoted by a multi-way array called as tensor. This review summarizes the current progress of tensor decomposition of EEG signals with three aspects. The first is about the existing modes and tensors of EEG signals. Secondly, two fundamental tensor decomposition models, canonical polyadic decomposition (CPD, it is also called parallel factor analysis-PARAFAC) and Tucker decomposition, are introduced and compared. Moreover, the applications of the two models for EEG signals are addressed. Particularly, the determination of the number of components for each mode is discussed. Finally, the N-way partial least square and higher-order partial least square are described for a potential trend to process and analyze brain signals of two modalities simultaneously. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.Journal of Neuroscience Methods 04/2015; 23. DOI:10.1016/j.jneumeth.2015.03.018 · 1.96 Impact Factor
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ABSTRACT: To propose a multi-domain feature extraction method of surface EMG signals is of great significance to EMG-based human-computer interface (HCI). In this paper, nonnegative Tucker decomposition (NTD)-one model of nonnegative tensor factorization (NTF)-is used to extract multidomain features of sEMG signals for classification. In the first step the sEMG data are transformed into multidimensional information using continuous wavelet transform and the 4-D sEMG tensor is established. Then the tensor is decomposed into four components (spatial components, spectral components, temporal components and category components) and the core tensor is the feature extracted. The feature after being eliminated and compressed are fed into KNN, LDA and SVM classifiers for the identification of condition classification. An experiment about elbow movements of 10 healthy participants was carried out to verify the validity of this algorithm. The result implied that NTF is a meaningful and valuable multidomain feature extraction method to EMG-based HCIs.2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 12/2013
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ABSTRACT: Measuring mental fatigue is essential in assessing the performance of those subjects whose careers involve severe mental activity. Recently, many analytical methods have been applied to electroencephalograms (EEGs) in order to quantitatively detect the fatigue state, but their accuracy is still not satisfactory. Factorization methods have been employed in our study to extract fatigue-related features from information captured from the ongoing raw EEG signals. The EEG signals were recorded from 32 channels from 17 healthy subjects before and after 3 h of severe mental activity. After preprocessing the raw EEGs, it was arranged in matrices to be decomposed by non-negative methods named NMF, LNMF, SNMF, DNMF, NTF, and DNTF. A comparative study of the methods was carried out by using support vector machine (SVM) (Sameni et al. in IEEE Trans Signal Process 58:2363-2374, 2010; Kadirgama et al. in Arab J Sci Eng 37:2269-2275, 2012) with extracted discriminative subspaces in order to classify raw EEGs into two "mental states" (fatigued/not fatigued). Experimental results demonstrated that discriminant DNTF outperformed (p < 0.05) the other compared non-negative methods in terms of accuracy, feature storage, and robustness.ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 10/2014; 39(10):7049-7058. DOI:10.1007/s13369-014-1242-0 · 0.37 Impact Factor