An adaptive entropy optimization algorithm for blind source separation

Blind Signal Separation II. Linear, Convolutive Combinations: II. Linear, Convolutive Combinations

Blind signal processing, especially independent component analysis (ICA) and multichannel blind deconvolution/equalization (MBD) problems have recently gained much interest due to many applications, especially in processing of biomedical signals (e.g. EEG, MEG, EMG, EOG, ECG), in wireless communications, 'cocktail party' problem, speech enhancement, geophysics, and source localization. In this paper both linear and nonlinear state space models for blind and semi-blind separation of linearly/nonlinearly mixed and filtered independent source signals are proposed. New unsupervised adaptive learning algorithms performing mutual independence of output signals are developed. For nonlinear mixture hyper radial basis function (HRBF), neural network proposed by Poggio and Girosi is employed and associated supervised-unsupervised learning rules for its parameters are developed.

In the last years Independent Component Analysis (ICA) has been applied with success in signal processing and many algorithms have been developed in order to perform ICA. In this paper we review some algorithms, like INFOMAX (Bell and Sejnowski 1995), extended-INFOMAX (Lee, Girolami and Sejniowski 1997), FastICA (OjA, and Hyvarinen 1999), that solve the ICA problem under the assumption of the linear mixture model. We also show an overview of the nonlinear ICA algorithms and we discuss the MISEP (Almeida 2003). In order to test the performances of the reviewed algorithms, we present some applications of ICA in biomedical signal processing. In particular the application of ICA to the electroencephalographic (EEG) and surface electromyographic (sEMG) recordings are shown.

A geometric algorithm for overcomplete linear ICA

Independent Component Analysis (ICA) algorithm is normallused for un-mixing and feature extraction of the fixed input block lengths. In case of varying block lengths re-adjustment of the maximum number of iterations and the step size parameter is required. In this paper, we introduced an Adaptive Step size Gradient Ascent ICA (AS-GAICA) technique for varying block length that can also controls the maximum number of iterations adaptively. The performance of the proposed technique is compared with Fast-ICA and Optimum Block Adaptation ICA (OBAICA) for telecommunication signals. Simulation results show that the proposed scheme outperforms the Fast-ICA and OBAICA algorithms.

Conventional methods for monitoring clinical (epileptiform) multichannel electroencephalogram (EEG) signals often involve morphological, spectral or time-frequency analysis on individual channels to determine waveform features for detecting and classifying ictal events (seizures) and inter-ictal spikes. Blind source separation (BSS) methods, such as independent component analysis (ICA), are increasingly being used in biomedical signal processing and EEG analysis for extracting a set of underlying source waveforms and sensor projections from multivariate time-series data, some of which reflect clinically relevant neurophysiological (epileptiform) activity. The work presents an alternative spatial approach to source tracking and detection in multichannel EEG that exploits prior knowledge of the spatial topographies of the sensor projections associated with the target sources. The target source sensor projections are obtained by ICA decomposition of data segments containing representative examples of target source activity, e.g. a seizure or ocular artifact. Source tracking and detection are then based on the subspace correlation between individual target sensor projections and the signal subspace over a moving window. Different window lengths and subspace correlation threshold criteria reflect transient or sustained target source activity. To study the behaviour and potential application of this spatial source tracking and detection approach, the method was used to detect (transient) ocular artifacts and (sustained) seizure activity in two segments of 25-channel EEG data recorded from one epilepsy patient on two separate occasions, with promising and intuitive results.

The analysis of functional magnetic resonance imaging (fMRI) signals using independent component analysis (ICA) has been a hotspot in the recent years. The main principle of performing the blind source separation (BSS) using spatial independent component analysis (SICA) is described. Since the fMRI signals from the experiments can be seen as a specific problem of BSS, the FastICA can be used to realize the BSS. Current analytical techniques, which are applied to fMRI signals, require a prior knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Without any prior knowledge about the time courses of processes contributing to the measured signals, only the FastICA can perform BSS and be used to separate fMRI signals into task-related independent components, head movement independent components, transient task-related independent components, noisy independent components and other independent component signals.

Independent component analysis (ICA) is regarded as a useful technique for processing a wide range of practical signals, such as speech, radar and biomedical recordings. In the biomedical image processing, functional magnetic resonance imaging become a common tool for investigating the brain function and cognitive process. However, much debate on the preferred technique for analyzing these functional activation images is still a problem. In this contribution, blind signal separation via ICA is proposed to detect the brain function activities. Several experiment with digital image data were also carried out based on the presented fastICA algorithm. ICA technique is employed to separate the independent components of the observation and restrain the impact caused by the additive noise. The results using common method and ICA technique were also demonstrated and compared to show that the proposed ICA method significantly reduces the physiological baseline fluctuation and the background interfaces.

The independent component analysis and wavelet transform technology are used to separate and identify the internal combustion engine noise signal. According to the basic principle of independent component analysis, FastICA based on negative entropy great with the good stability and convergence speed algorithm is applied to separate the noise signals of six cylinder diesel engine. And the noise signals are decomposed into a series of independent components. The fast Fourier transform and wavelet transform technology are applied on each independent component analysis. Combining with the time-frequency analysis results and the internal combustion engine noise signal spectrum and the structure of the separation of the independent component and getting the corresponding relationship of different internal combustion engine noise sources. The results show that: the independent components correspond to the diesel engine combustion noise sources, the piston knock noise, and fuel injection pump noise and so on.

Electrocardiogram (ECG) signal processing aims basically 1) at artifact reduction to make the ECG signals cleaner and better interpretable by human or machine observers, 2) at revealing aspects not immediately observable in plain measured ECG signals even after artifact reduction, or 3) at diagnostics decision support and automated ECG signal interpretation, including classification of ECG signals into different classes associated with normal or pathological heart function. Thus, sports related applications aside, body surface ECG signal processing aims at enhancing ECG based diagnostics. In this Chapter, we review and demonstrate a statistical signal processing approach, independent component analysis (ICA), which is inherently very suitable for ECG signal processing regarding the aims 1) and 2) above, and also equally applicable as a component in systems aimed at accomplishing the aim 3). For more on general ECG signal processing, the reader is directed to the textbook written by Sornmo & Laguna (2005), and for a thorough treatment of ICA to the Hyvarinen’s book (Hyvarinen et al., 2001). A concise review on ICA in ECG signal processing has been presented by Castells et al. (2007a). In this Chapter, we describe and illustrate several widely adopted applications of ICA in ECG signal processing, and discuss associated practical aspects, some of which are not generally found in the literature. The treatment of the matter is aimed at conceptual and practical understanding, leaving the mathematical derivations and proofs far mostly for the interested reader to find in the references. ICA (Castells et al., 2007a; Comon, 1994; Hyvarinen et al., 2001; Hyvarinen & Oja, 2000; Naik & Kumar, 2011) is a statistical signal processing method for decomposing a set of signals into a set of mutually independent component signals. In general, in the applications of ICA, including in ECG signal processing, the objective is that the resulting independent component signals are the original source signals. Since ICA operates purely based on the input signals and a few assumptions, ICA belongs to the class of methods called blind source separation methods. For ICA, a source signal is called an independent component (IC). The terms ‘IC’ and ‘source signal’ are here used interchangeably. For ECG, the source signals are the bioelectrical signals generated by the heart, and all the possible artifact signals. Generally, ICA input signals are the observed signals, which may be measurement time series, such as sampled voltage values in time as in the case of ECGs, image pixel values, or basically any sets of values fulfilling the assumptions of ICA. In the sequel, the term

Independent Component Analysis (ICA) is used to separate the relatively independent signals from the source. Gravity tide signal is a mixed signal which is caused by the moon and the sun, so ICA is applied to separate gravity tide signal in this paper. Because of the slowly convergence speed and local search for the target in ICA, Particle Swarm Optimization which could find target globally and quickly is proposed to search the optical target in ICA. Simulation results show that the method proposed in this paper solves the problem presented in ICA and separates gravity tide signal into three parts. Every part represents the signal which is corresponding to the theory frequencies of the harmonic component in gravity tide signal. So the method proposed in the paper can divide the signal into three parts which is corresponding to harmonic component automatically.

ERP is commonly obtained by averaging over segmented EEC epochs. In case artifacts are present in the raw EEC measurement, pre-processing is required to prevent the averaged ERP waveform being interfered by artifacts. The simplest pre-processing approach is by rejecting trials in which presence of artifact is detected. Alternatively artifact correction instead of rejection can be performed by blind source separation, so that waste of ERP trials is avoided. In this paper, we propose a second order statistics based blind source separation approach to ERP artifact correction. Comparing with blind separation using independent component analysis, second order statistics based method does not rely on higher order statistics or signal entropy, and therefore leads to more robust separation even if only short epochs are available.

We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (1) frequency-domain ICA with direction-of-arrival (DOA) estimation, (2) null beamforming based on the estimated DOA, and (3) integration of (1) and (2) based on the algorithm diversity in both iteration and frequency domain. The inverse of the mixing matrix obtained by ICA is temporally substituted by the matrix based on null beamforming through iterative optimization, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.

The technique of blind source separation (BSS) has been well studied. In this paper, we apply the BSS technique, particularly based on independent component analysis (ICA), to a meeting situation. The goal is to enhance the spoken utterances and to estimate the location of each speaker by means of multiple microphones. The technique may help us to take the minutes of a meeting.

We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (1) frequency-domain ICA with direction-of-arrival (DOA) estimation; (2) null beamforming based on the estimated DOA; (3) integration of (1) and (2) based on the algorithm diversity in both iteration and frequency domain. The inverse of the mixing matrix obtained by ICA is temporally substituted by the matrix based on null beamforming through iterative optimization, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.