Nonnegative Tensor Factorization for Continuous EEG Classification

Department of Computer Science, Pohang University of Science and Technology, San 31 Hyoja-dong, Nam-gu, Pohang 790-784, Republic of Korea.
International Journal of Neural Systems (Impact Factor: 6.51). 09/2007; 17(4):305-17. DOI: 10.1142/S0129065707001159
Source: PubMed


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.

Download full-text


Available from: Lee Hyekyoung, Jun 24, 2014
39 Reads
  • Source
    • "In the recent years, a lot of attention has been paid to blind source separation (BSS) due to its wide-ranging applications in many areas [1] such as audio and speech processing [2], telecommunications [3], biomedical engineering [4], hyperspectral imaging [5], etc. Assuming an M -dimensional observation vector, y(k), this problem is mathematically expressed as: "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper addresses the problem of blind source separation for underdetermined mixtures (i.e., more sources than sensors) of event-related sources that include quasi-periodic sources (e.g., electrocardiogram (ECG)), sources with synchronized trials (e.g., event-related potentials (ERP)), and amplitude-variant sources. The proposed method is based on two steps: (i) tensor decomposition for underdetermined source separation and (ii) signal extraction by Kalman filtering to recover the source dynamics. A tensor is constructed for each source by synchronizing on the “event” period of the corresponding signal and stacking different periods along the second dimension of the tensor. To cope with the interference from other sources that impede on the extraction of weak signals, two robust tensor decomposition methods are proposed and compared. Then, the state parameters used within a nonlinear dynamic model for the extraction of event-related sources from noisy mixtures are estimated from the loading matrices provided by the first step. The influence of different parameters on the robustness to outliers of the proposed method is examined by numerical simulations. Applied to clinical electroencephalogram (EEG), ECG and magnetocardiogram (MCG), the proposed method exhibits a significantly higher performance in terms of expected signal shape than classical source separation methods such as π CA and FastICA.
    Signal Processing 08/2014; 101:52–64. DOI:10.1016/j.sigpro.2014.01.031 · 2.21 Impact Factor
  • Source
    • "To our knowledge, EEG artifact removal within a joint processing framework of single-channel EEG and auxiliary recordings is completely novel. Also, while NTF has already been used for EEG-feature extraction [7], its use for EEG artifact removal within a Gaussian source separation framework is novel. "
    [Show abstract] [Hide abstract]
    ABSTRACT: New applications of Electroencephalographicrecording (EEG) require light and easy-to-handle equipment involving powerful algorithms of artifact removal. In our work, we exploit informed source separation methods for artifact removal in EEG recordings with a low number of sensors, especially in the extreme case of single-channel recording, by exploiting prior knowledge from auxiliary lightweight sensors capturing artifactual signals. To achieve this, we propose a method using Non-negative Tensor Factorization (NTF) in a Gaussian source separation framework that proves competitive against the classic Independent Component Analysis (ICA) technique. Additionally the both NTF and ICA methods are used in an original scheme that jointly processes the EEG and auxiliary signals. The adopted NTF strategy is shown to improve the source estimates accuracy in comparison with the usual multi-channel ICA approach.
    IEEE International Workshop on Machine Learning for Signal Processing; 09/2013
  • Source
    • "Sparse coding is becoming a hot topic in image analysis. It has already been used in pattern identification tasks for audio source separation [23], face recognition [24] [25], texture analysis [26], MR image classification [27] [28], etc. The ideas of sparse coding presented in this paper are used to illustrate the dimensionality reduction application, and optimization processing in texture synthesis for shape identification purposes. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This study explores an approach for analysing the mirror (reflective) symmetry of 3D shapes with tensor based sparse decomposition. The approach combines non-negative tensor decomposition and directional texture synthesis, with symmetry information about 3D shapes that is represented by 2D textures synthesised from sparse, decomposed images. This technique requires the center of mass of 3D objects to be at the origin of the coordinate system. The decomposition of 3D shapes and analysis of their symmetry are useful for image compression, pattern recognition, as well as there being an emerging interest in the medical community due to its potential to find morphological changes between healthy and pathological structures. This paper postulates that sparse texture synthesis can be used to describe the decomposed basis images acting as symmetry descriptors for a 3D shape. We apply the theory of non-negative tensor decomposition and sparse texture synthesis, deduce the new representation, and show some application examples.
    Computer methods and programs in biomedicine 11/2012; 108(2):629-643. DOI:10.1016/j.cmpb.2011.10.007 · 1.90 Impact Factor
Show more