Single-channel EEG signal extraction based on DWT, CEEMDAN, and ICA method

<span>The purpose of this research is to observe the effectiveness of independent component analysis (ICA) method for denoising raw EEG signals based on word imagination, which will be used for word classification on unspoken speech. The electroencephalogram (EEG) signals are signals that represent the electrical activities of the human brain when someone is doing activities, such as sleeping, thinking or other physical activities. EEG data based on the word imagination used for the research is accompanied by artifacts, that come from muscle movements, heartbeat, eye blink, voltage and so on. In previous studies, the ICA method has been widely used and effective for relieving physiological artifacts. Artifact to signal ratio (ASR) is used to measure the effectiveness of ICA in this study. If the ratio is getting larger, the ICA method is considered effective for clearing noise and artifacts from the EEG data. Based on the experiment, the obtained ASR values from 11 subjects on 14 electrodes amounted are within the range of 0,910 to 1,080. Thus, it can be concluded that ICA is effective for removing artifacts from EEG signals based on word imagination.</span>

Two distinct topics are dealt with. First, a new method for independent component analysis (ICA) has been constructed that exploits the invariance of criteria under component-wise scaling, which is intrinsic to ICA. This practical and simple ICA method is called the nested Newton's method. When the number of the channel of observation is less than a certain level, factor analysis (FA) is ineffective (bound for FA). The target of this paper is these cases. Three of many concrete advantages of the nested Newton's method are addressed. i) It is robust against Gaussian noise and outperforms existing methods, such as JADE and Fast ICA, especially under Gaussian noise conditions. ii) It is highly stable globally. iii) Each step resolves itself into two-dimensional (2-D) matrix problems. There is thus no need to deal with gigantic matrices, which means that fewer computational resources are required. Second, a method called "post factor analysis (post-FA)" is described that is aimed to be useful as post-processing for ICA. Although it is functionally similar to conventional FA, post-FA is a completely new method and is more powerful than conventional FA in compensation for its stronger assumption that there are mutually independent sources behind observations. By fully making use of this assumption, post-FA is capable of estimating the noise variance beyond the known limit for FA. Furthermore, it improves the accuracy of ICA to a considerable extent. Any ICA algorithm without prewhitening (pre-WH) or pre-factor-analysis (pre-FA) can be used for preprocessing, although the nested method is a good candidate.

The impact of outliers on signal separation performance of an independent component analysis (ICA) algorithm is an important factor when selecting an ICA algorithm. If an ICA estimator has the property of B-robustness, the influence of an extreme point is bounded, leading to good separation performance in the presence of outliers. Since this property is binary, it does not give the degree of influence an outlier has on the separation performance. To address this issue, a Mamdani-type fuzzy inference, based on the location of a potential outlier and on the skewness of the data set, has been developed. It creates an outlier sensitivity map for an ICA algorithm. The implication of this work is a criterion to switch from one ICA algorithm to another in real time, as determined by the algorithms sensitivity to the data set under consideration. This paper describes estimation of the outlier impact on the separation performance of the non-B-robust FastICA algorithm using a Mamdani-type fuzzy inference. In simulations with data sets contaminated by outliers, the FastICA sensitivity map resembles the separation performance measured by the Amari performance index.

The higher-order statistics based independent component analysis (ICA) algorithm can extract natural image features. Based on the maximum likelihood ICA criterion, and using the weighted orthogonal constrained natural gradient, a new ICA algorithm is proposed. Natural image feature extraction simulation results show that, compared with other ICA algorithms, the proposed algorithm has faster convergence rate, most of the extracted basis vectors are localized in space, frequency, and orientation, which can describe the features of the natural images well, and the corresponding coefficients are very sparse, obey stronger super-Gaussian distribution with very high kurtosis.

Computer displays emit electromagnetic waves, which compromise the information displayed by the computer. This can be a potential information security threat as the sensitive information can be stolen from a distance without leaving any trace. The video leakage signals contain the information of the image displayed in the computer, so the video leakage signals can be seen as special image signals. However, different from the normal image signal, the signal to noise ratio (SNR) of video leakage signal is low due to the environmental noise and many other man-made noises. In this paper, a novel wavelet based independent component analysis (ICA) method is proposed for improving SNR of computer video leakage signals. By using this method, we can improve the performance of pre-processing of computer video leaking signals. We solve the problem of using Fast ICA in processing video leakage signal by using a wavelet filter. The performance of Fast ICA is improved by working in wavelet domain because of advantages like ease of implementation and less computation time when compared to time domain. We pre-process one-dimensional received signal without reconstructing the image since it is inefficient to reconstruct signal before processing. A direct SNR can't be defined. Therefore, another metric called quasi signal to noise ratio (QSNR) is defined to estimate signal to noise ratio of video leakage signals. The processed results of the actual experimental data show that the proposed wavelet based ICA algorithm has a better performance than the Fast ICA algorithm and the Wavelet denoising algorithm.

Our goal is to develop a novel BCI based on an eye movements system employing EEG signals on-line. Most of the analysis on EEG signals has been performed using ensemble averaging approaches. However,It is suitable to analyze raw EEG signals in signal processing methods for BCI. In order to process raw EEG signals, we used independent component analysis(ICA). However, we do not know which ICA algorithms have good performance. It is important to check which ICA algorithms have good performance to develop BCIs. Previous paper presented extraction rate of saccade-related EEG signals by five ICA algorithms and eight window size. However, three ICA algorithms, the FastICA, the NG-FICA and the JADE algorithms, are based on 4th order statistic and AMUSE algorithm has an improved algorithm named SOBI.Therefore, we must re-select ICA algorithms. In this paper, we add new algorithms; the SOBI and the MILCA. The SOBI is an improved algorithm based on the AMUSE and uses at least two covariance matrices at different time steps. The MILCA use the independency based on mutual information. Using the Fast ICA, the JADE, the AMUSE, the SOBI, and the MILCA, we extract saccade-related EEG signals and check extracting rates. Secondly, in order to get more robustness against EOG noise, we use improved FastICA with reference signals and check extracting rates.

As an important factor in the fast fixed-point algorithm of independent component analysis (ICA), noise has a significant influence on the separate performance of ICA. Unfortunately, the traditional algorithm of noisy ICA did not address the influence of impulsive components. Because the sources were signals mixed with impulsive noise, the Gaussian noisy algorithm will be invalid for separating the sources. In general, those measurements that significantly deviate from the normal pattern of sensed data are considered impulses. In this paper, we introduce a non-linear function based on the S-estimator to identify the impulsive components in the observed data. This approach guarantees that the impulse noise can be detected from the observed signal. Furthermore, a threshold for the impulse components and methods to remove impulse noise and reconstruct the signal is proposed. The proposed technique improves the separate performance of the traditional algorithm for Gaussian noisy ICA. With the proposed method, the fast fixed-point algorithm of ICA is more reliable for noisy situations. The simulation results show the effectiveness of the proposed method.

Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from real-world measured data and represent them corresponding to the human brain functions. This usually depends on how to reduce a high level noise from the measurement. In this paper, a novel multistage MEG data analysis method based on the empirical mode decomposition (EMD) and independent component analysis (ICA) approaches is proposed for the feature extraction. Moreover, EMD and ICA algorithms are investigated for analyzing the MEG single-trial data which is recorded from the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in high level noise reduction by EMD associated with ICA approach and source localization by equivalent current dipole fitting method.

The purpose of this paper is to contrast, in terms of standard criteria for statistical independence, cumulant-based methods of independent component analysis (ICA) with a potentially more robust blind signal separation technique called BLISS. This is able to separate independent non-Gaussian co-channel signals from multisensor data using only the joint probability distributions of instantaneous linear mixtures of those signals. BLISS is also able, without prior array calibrations or training waveforms, to estimate individual steering vectors including unknown mutual coupling and multipath. We point out fundamental reasons for the difficulty of comparing the performance of different ICA algorithms on finite duration practical data. We also propose a novel method for applying real-valued ICA to complex-valued data. By separately estimating in-phase and quadrature un-mixing parameters, we avoid the difficulty of selecting a subset of real and complex-valued cumulants. To justify our approach, we extend the definition of independence to the complex-valued case.

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

We apply the independent component analysis (ICA) algorithm to uncover intrinsic noise in the beam position monitor (BPM) system. Numerical simulations found that ICA is efficient in the BPM noise estimation. The ICA algorithm is applied to the turn-by-turn data at the Relativistic Heavy Ion Collider. We found the distribution of the BPM noise level, which is consistent with the Johnson-Nyquist thermal noise model. The ICA analysis of turn-by-turn data can be used in neuronetwork feasibility of monitoring a storage ring parasitically.

The electroencephalogram (EEG) is a set of data measured by electrodes placed on the scalp and is often under the influences of artifacts. Mental EEG is recorded when a person performs different mental tasks. In this article, we separated the mental EEG signals into independent components with individual meanings based on Independent Component Analysis (ICA) method, and the EEG was reconstructed by excluding those components related to ocular and line artifacts for further feature extraction. In the end, we made a simulation to compare denoising of mental EEG using ICA with the result using wavelet packet analysis. The ICA method was found to be more efficient than the classical methods.

Automatic facial feature extraction is one of the most important and attempted problems in computer vision. It is a necessary step in face recognition, facial image compression. There are many methods have been proposed in the literature for the facial feature extraction task. However, all of them have still disadvantage such as not complete reflection about face structure, face texture. Therefore, a combination of different feature extraction methods which can integrate the complementary information should lead to improve the efficiency of feature extraction stage. In this paper we describe a methodology for improving the efficiency of feature extraction stage based on the association of two methods: geometric feature based method and Independent Component Analysis (ICA) method. Comparison of two methods of facial feature extraction: geometric feature based method combined with PCA method (called GPCA) versus geometric feature based method combined with ICA method (called GICA) on CalTech dataset has demonstrated the efficiency of GICA method. Our results show that GICA achieved good performance 96.57% compared to 94.70% of GPCA method. Furthermore, we compare two methods mentioned above on our dataset, with performance of GICA being 98.94% better 96.78% of GPCA method. The experiment results have confirmed the benefits of the association geometric feature based method and ICA method in facial feature extraction.KeywordsFace recognitionindependent component analysis (ICA)principal component analysis (PCA)geometric features

Nonnegative matrix factorization (NMF) is known as a parts-based linear representation for nonnegative data. This method has been applied for blind source separation (BSS) when the sources are nonnegative. This paper presents a new NMF method for independent component analysis (ICA), which is useful for BSS without the nonnegativity constraint. Using this method, we transform the sources by their cumulative distribution functions (CDFs) and perform the nonparametric quantization to construct a nonnegative matrix where each entry represents the joint probability density of two transformed signals. The NMF procedure is accordingly realized to find the ICA demixing matrix. The independence between sources is maximized towards attaining the uniformity in the joint probability density. In the experiments on the separation of signal and music signals, we show the effectiveness of the proposed NMF-ICA compared to the infomax ICA and FastICA algorithms.

The EEG signals (electroencephalogram) are one of the most used biosignals in diagnosis of neurological disorders and technology like brain computer interface (BCI). The main problem in the use of EEG signals is that are often contaminated by physiological and non-physiological artifacts. EOG (electrooculogram) artifact is one of the first origins of artifacts in EEG recordings. It severely affects the EEG signal and modifies the shape of the neural activity that drive the BCI system affecting then its accuracy and performance. This paper presents an original algorithm to detect and remove EOG artifacts from EEG signals. In the proposed method, the statistical parameters extracted from the signal are used as the inputs of a fuzzy inference system to take a decision if an epoch of EEG is contaminated by the EOG or not. Thereafter, the level-5 stationary wavelet transform (SWT) is applied on the detected contaminated epochs to remove the artifacts without bringing much distortion to the neural activity. The performance of the developed algorithm is demonstrated in two scenarios: semi-simulated artifactual EEG data and fully real artifactual EEG data. The presented results show that our algorithm outperforms some existing algorithms.