288 research outputs found
Kurtosis-Based Blind Source Extraction of Complex Non-Circular Signals with Application in EEG Artifact Removal in Real-Time
A new class of complex domain blind source extraction algorithms suitable for the extraction of both circular and non-circular complex signals is proposed. This is achieved through sequential extraction based on the degree of kurtosis and in the presence of non-circular measurement noise. The existence and uniqueness analysis of the solution is followed by a study of fast converging variants of the algorithm. The performance is first assessed through simulations on well understood benchmark signals, followed by a case study on real-time artifact removal from EEG signals, verified using both qualitative and quantitative metrics. The results illustrate the power of the proposed approach in real-time blind extraction of general complex-valued sources
Electroencephalograph (EEG) signal processing techniques for motor imagery Brain Computer interface systems
Brain-Computer Interface (BCI) system provides a channel for the brain to
control external devices using electrical activities of the brain without using the
peripheral nervous system. These BCI systems are being used in various medical
applications, for example controlling a wheelchair and neuroprosthesis devices for
the disabled, thereby assisting them in activities of daily living. People suffering
from Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis and completely locked
in are unable to perform any body movements because of the damage of the
peripheral nervous system, but their cognitive function is still intact. BCIs operate
external devices by acquiring brain signals and converting them to control
commands to operate external devices. Motor-imagery (MI) based BCI systems, in
particular, are based on the sensory-motor rhythms which are generated by the
imagination of body limbs. These signals can be decoded as control commands in
BCI application. Electroencephalogram (EEG) is commonly used for BCI applications
because it is non-invasive. The main challenges of decoding the EEG signal are
because it is non-stationary and has a low spatial resolution. The common spatial
pattern algorithm is considered to be the most effective technique for
discrimination of spatial filter but is easily affected by the presence of outliers.
Therefore, a robust algorithm is required for extraction of discriminative features
from the motor imagery EEG signals.
This thesis mainly aims in developing robust spatial filtering criteria which
are effective for classification of MI movements. We have proposed two approaches
for the robust classification of MI movements. The first approach is for the
classification of multiclass MI movements based on the thinICA (Independent
Component Analysis) and mCSP (multiclass Common Spatial Pattern Filter) method.
The observed results indicate that these approaches can be a step towards the
development of robust feature extraction for MI-based BCI system.
The main contribution of the thesis is the second criterion, which is based on
Alpha- Beta logarithmic-determinant divergence for the classification of two class
MI movements. A detailed study has been done by obtaining a link between the AB
log det divergence and CSP criterion. We propose a scaling parameter to enable a
similar way for selecting the respective filters like the CSP algorithm. Additionally,
the optimization of the gradient of AB log-det divergence for this application was
also performed. The Sub-ABLD (Subspace Alpha-Beta Log-Det divergence)
algorithm is proposed for the discrimination of two class MI movements. The
robustness of this algorithm is tested with both the simulated and real data from BCI
competition dataset. Finally, the resulting performances of the proposed algorithms
have been favorably compared with other existing algorithms
Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases
Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems
Convolutive Blind Source Separation Methods
In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks
Development of Novel Independent Component Analysis Techniques and their Applications
Real world problems very often provide minimum information regarding their causes. This is mainly due to the system complexities and noninvasive techniques employed by scientists and engineers to study such systems. Signal and image processing techniques used for analyzing such systems essentially tend to be blind. Earlier, training signal based techniques were used extensively for such analyses. But many times either these training signals are not practicable to be availed by the analyzer or become burden on the system itself. Hence blind signal/image processing techniques are becoming predominant in modern real time systems. In fact, blind signal processing has become a very important topic of research and development in many areas, especially biomedical engineering, medical imaging, speech enhancement, remote sensing, communication systems, exploration seismology, geophysics, econometrics, data mining, sensor networks etc. Blind Signal Processing has three major areas: Blind Signal Separation and Extraction, Independent Component Analysis (ICA) and Multichannel Blind Deconvolution and Equalization. ICA technique has also been typically applied to the other two areas mentioned above. Hence ICA research with its wide range of applications is quite interesting and has been taken up as the central domain of the present work
Adaptive signal processing algorithms for noncircular complex data
The complex domain provides a natural processing framework for a large class of signals
encountered in communications, radar, biomedical engineering and renewable
energy. Statistical signal processing in C has traditionally been viewed as a straightforward
extension of the corresponding algorithms in the real domain R, however,
recent developments in augmented complex statistics show that, in general, this leads
to under-modelling. This direct treatment of complex-valued signals has led to advances
in so called widely linear modelling and the introduction of a generalised
framework for the differentiability of both analytic and non-analytic complex and
quaternion functions. In this thesis, supervised and blind complex adaptive algorithms
capable of processing the generality of complex and quaternion signals (both
circular and noncircular) in both noise-free and noisy environments are developed;
their usefulness in real-world applications is demonstrated through case studies.
The focus of this thesis is on the use of augmented statistics and widely linear modelling.
The standard complex least mean square (CLMS) algorithm is extended to
perform optimally for the generality of complex-valued signals, and is shown to outperform
the CLMS algorithm. Next, extraction of latent complex-valued signals from
large mixtures is addressed. This is achieved by developing several classes of complex
blind source extraction algorithms based on fundamental signal properties such
as smoothness, predictability and degree of Gaussianity, with the analysis of the existence
and uniqueness of the solutions also provided. These algorithms are shown
to facilitate real-time applications, such as those in brain computer interfacing (BCI).
Due to their modified cost functions and the widely linear mixing model, this class of
algorithms perform well in both noise-free and noisy environments. Next, based on a
widely linear quaternion model, the FastICA algorithm is extended to the quaternion
domain to provide separation of the generality of quaternion signals. The enhanced
performances of the widely linear algorithms are illustrated in renewable energy and
biomedical applications, in particular, for the prediction of wind profiles and extraction
of artifacts from EEG recordings
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