nonlinear independent component analysis for eeg based brain computer interface systems

... Bournemouth, UK, September 2004 [10] A Erfanian and A Erfani, “ICA -based classification scheme for EEG- based brain- computer interface: the role of mental practice and concentration skills,” in ... a novel approach for feature extraction which is based on mutual information The goal of mutual information -based feature extraction (MIFX) is to create new features from transforming the original ... Data Engineering, vol 15, no 6, pp 1374–1388, 2003 [9] A Erfanian and A Erfani, EEG- based brain- computer interface for hand grasp control: feature extraction by using ICA,” in Proceedings of the...

Ngày tải lên: 21/06/2014, 22:20

... multispectral and spatial information can be fully explored by a statistical independency -based transform, called independent component analysis (ICA) and feature extraction -based classification techniques ... linearly transform three band MR images into three statistically independent component images so that these three ICA-generated independent components (ICs) can be stacked one atop another to form a ... Karhunen, and E Oja, Independent Component Analysis, John Wiley & Sons, New York, NY, USA, 2001 [18] T Nakai, S Muraki, E Bagarinao, et al., “Application of independent component analysis to magnetic...

Ngày tải lên: 21/06/2014, 22:20

... ICA for Audio Signals 137 Masoud Geravanchizadeh and Masoumeh Hesam Section ICA: Biomedical Applications 163 Chapter Nonlinear Independent Component Analysis for EEG- Based Brain- Computer Interface ... Introduction: Independent Component Analysis Introduction: Independent Component Analysis 21 19 Mackay, D J C (1996) Maximum likelihood and covariant algorithms for independent component analysis, ... (2001) Independent Component Analysis, Wiley-Interscience James, C J & Hesse, C W (2005) Independent component analysis for biomedical signals, Physiological Measurement 26(1): R15+ 20 18 Independent...

Ngày tải lên: 29/06/2014, 13:20

... Bayes PCA Principal component analysis PC-ICA Partition-conditional independent component analysis TCA Tree-dependent component analysis TICA Topographic independent component analysis SVM Support ... Table of Contents 3.3.2 Principal component analysis 43 3.2.3 Independent component analysis 44 3.2.4 Class-conditional independent component analysis 48 3.3 EMPIRICAL COMPARISON ... discrimination of mental tasks for EEG- based brain computer interface systems It was found that ICA integrated with the SVM classifier may produce good classification performance, which could be attributed...

Ngày tải lên: 11/09/2015, 10:02

... in an ERP -based braincomputer interface Behavioral and Brain Functions, 6(1):28 J.R Wolpaw, N Birbaumer, D.J McFarland, G Pfurtscheller, and T.M Vaughan 2002 Braincomputer interfaces for communication ... typing interface difficult or impossible to use for the most impaired users Simpler interactions via brain- computer interfaces (BCI) hold much promise for effective text communication for these ... classification accuracy for the EEG- based classifier The information obtained from showing the visual stimuli multiple times can easily be combined by assuming the trials are statistically independent, as...

Ngày tải lên: 20/02/2014, 05:20

... therefore from any computer with internet connection the house can be controlled 22 Recent Advances in Brain- Computer Interface Systems Fig 13 BCI interface and web interface A light is for example ... classification of EEG signal for brain- computer interface IEEE Trans Neural Syst Rehabil Eng., 14(1): p 24-29 Vallabhaneni, A., “Motor imagery task classification for brain computer interface applications ... Farid Oveisi and Ali Shadvar Chapter P300 -based Brain- Computer Interface Paradigm Design 83 Reza Fazel-Rezai and Waqas Ahmad Chapter Brain Computer Interface Based on the Flash Onset and Offset Visual...

Ngày tải lên: 27/06/2014, 06:20

... LEARNING APPROACH TO NONLINEAR BSS In this section, we present a new generative approach for nonlinear blind source separation or independent component analysis Here the nonlinear mapping (17.2) ... mixtures [438] 17.1.2 Existence and uniqueness of nonlinear ICA The question of existence and uniqueness of solutions for nonlinear independent component analysis has been addressed in [213] The authors ... : : : xn into n independent components y1 : : : yn , giving a solution for the nonlinear ICA problem This construction also clearly shows that the decomposition in independent components is by...

Ngày tải lên: 20/01/2014, 11:20

... human brain, both for research and clinical purposes It is in fact one of the most widespread brain mapping techniques to date EEG is used both for the measurement of spontaneous activity and for ... basically very similar information to EEG, but with a higher spatial resolution MEG is mainly used for basic cognitive brain research To measure the weak magnetic fields of the brain, superconducting ... sources corresponding to the independent components fell on the brain regions expected to be activated by the particular stimulus Applications of ICA have been proposed for analysis of other kinds...

Ngày tải lên: 20/01/2014, 11:20

... Sejnowski, “Viewpoint invariant face recognition using independent component analysis and attractor networks,” in Advances in Neural Information Processing Systems 9, pp 817–823, MIT Press, Cambridge, ... Mouchot, and L HubertMoy, Independent component analysis as a tool for the 12 [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] EURASIP Journal on Embedded Systems dimensionality ... Symposium on Circuits and Systems (ISCAS ’04), vol 5, pp 760–763, Vancouver, Canada, May 2004 G Cauwenberghs, “Neuromorphic autoadaptive systems and independent component analysis, ” Tech Rep N00014-99-10612,...

Ngày tải lên: 22/06/2014, 22:20

... Frequency Frequency Frequency S1 ( f , t) component S2 ( f , t) component S1 ( f , t) component S2 ( f , t) component S1 ( f , t) component S2 ( f , t) component (a) (b) (c) Gain Gain Gain Figure ... Frequency Frequency Frequency S1 ( f , t) component S2 ( f , t) component S1 ( f , t) component S2 ( f , t) component S1 ( f , t) component S2 ( f , t) component (a) (b) (c) Figure 4: Examples ... target component power in the output signal and the interference component power We obtain these components by inputting SIMO-model -based signals [A1l ( f )Sl ( f , t), , AKl ( f )Sl ( f , t)] for...

Ngày tải lên: 22/06/2014, 23:20

... model used Therefore, it has been proposed that the independent component analysis (ICA) model should include a noise term as well In this chapter, we consider different methods for estimating ... noise in the data before performing ICA For example, simple filtering of time-signals is often very useful in this respect, and so is dimension reduction by principal component analysis (PCA); see ... of the independent components (ICs) The noisy model is not invertible, and therefore estimation of the noise-free components requires new methods This problem leads to some interesting forms of...

Ngày tải lên: 17/10/2013, 19:15

... ESTIMATION OF THE INDEPENDENT COMPONENTS Maximum likelihood estimation Many methods for estimating the mixing matrix use as subroutines methods that estimate the independent components for a known ... Therefore, we shall first treat methods for reconstructing the independent components, assuming that we know the mixing matrix Let us denote by m the number of mixtures and by n the number of independent ... the number of independent components Thus, the mixing matrix has size m n with n > m, and therefore it is not invertible The simplest method of estimating the independent components would be to...

Ngày tải lên: 20/10/2013, 10:15

... LEARNING APPROACH TO NONLINEAR BSS In this section, we present a new generative approach for nonlinear blind source separation or independent component analysis Here the nonlinear mapping (17.2) ... mixtures [438] 17.1.2 Existence and uniqueness of nonlinear ICA The question of existence and uniqueness of solutions for nonlinear independent component analysis has been addressed in [213] The authors ... : : : xn into n independent components y1 : : : yn , giving a solution for the nonlinear ICA problem This construction also clearly shows that the decomposition in independent components is by...

Ngày tải lên: 20/10/2013, 10:15

... components This is why in basic ICA, we have to use the nongaussian structure of the independent components, for example, by minimizing the higher-order dependencies as measured by mutual information ... g=0 ) for all i j (18.4) The motivation for this is that for the ICs si (t), the lagged covariances are all zero due to independence Using these lagged covariances, we get enough extra information ... contrast to ICA using higher-order information, where the independent components are allowed to have identical distributions Further work on using autocovariances for source separation can be found...

Ngày tải lên: 24/10/2013, 08:15

... to use complex-valued data Therefore we present most methods for this general case The respective algorithms for real data are obtained as special cases Methods for estimating the ICA model with ... values of the original signal s(t) were assumed to be independent for different t and nongaussian Therefore, the blind deconvolution problem is formally closely related to the standard ICA problem ... out for each mixture, the data model for convolutive mixtures is given by A As XX x n xi (t) = j =1 aikj sj (t k) for i = ::: n (19.19) k This is a FIR filter model, where each FIR filter (for...

Ngày tải lên: 24/10/2013, 08:15

... distributions One solution for this problem is given by independent subspaces analysis, to be explained next 20.2.2 Independent subspace analysis Independent subspace analysis [204] is a simple ... that the components si are independent However, ICA is often applied on data sets, for example, on image data, in which the obtained estimates of the independent components are not very independent, ... of the independent components This is due to the fact that often the independent components not form a well-defined set Especially in image decomposition (Chapter 21), the set of potential independent...

Ngày tải lên: 28/10/2013, 15:15

... later use this model for processing the signals, for example, to compress or denoise them Naturally, we shall use independent component analysis (ICA) as the principal model for natural images ... same phase 21.2 ICA AND SPARSE CODING The transforms just considered are fixed transforms, meaning that the basis vectors are fixed once and for all, independent of any data In many cases, however, ... estimate a linear sparse coding transformation of the data by formulating a measure of sparseness of the components, and maximizing the measure in the set of linear transformations In fact, since sparsity...

Ngày tải lên: 28/10/2013, 15:15

... human brain, both for research and clinical purposes It is in fact one of the most widespread brain mapping techniques to date EEG is used both for the measurement of spontaneous activity and for ... basically very similar information to EEG, but with a higher spatial resolution MEG is mainly used for basic cognitive brain research To measure the weak magnetic fields of the brain, superconducting ... sources corresponding to the independent components fell on the brain regions expected to be activated by the particular stimulus Applications of ICA have been proposed for analysis of other kinds...

Ngày tải lên: 07/11/2013, 09:15

... CDMA technology is therefore a strong candidate for future global wireless communications systems For example, it has already been chosen as the transmission technique for the European third generation ... matrix, and qm = f (bm ) is a nonlinearly transformed symbol vector bm The nonlinear function f is typically a sigmoidal or cubic nonlinearity, and it is applied componentwise to the elements ... principal component analysis of temporal correlation matrices This method is actually just another example of blind source separation approaches based on second-order temporal statistics; for example...

Ngày tải lên: 07/11/2013, 09:15

... techniques, the smoothing was nonlinear Using nonlinear smoothing, optimized for each independent component time series separately, the prediction of the ICs is more accurately performed and the results ... algorithm The number of ICs can be variable For each component sj (t), a suitable nonlinear filtering is applied to reduce the effects of noise — smoothing for components that contain very low frequencies ... classic AR prediction method for each independent component In reality, especially in real world time series analysis, the data are distorted by delays, noise, and nonlinearities Some of these...

Ngày tải lên: 07/11/2013, 09:15