Background The new emerging frontier of technology modeling, cyber-physical systems (CPS), is rel... more Background The new emerging frontier of technology modeling, cyber-physical systems (CPS), is related to the generation of new systems with the integration of the physical systems and real-time computational intelligence capabilities. This creates a fully intelligent system to tackle a variety of application fields such as robotics, intelligent buildings, biomedical and healthcare, energy, manufacturing control systems, and avionics [1, 2]. In biomedical CPS fields, especially in cognitive neuroscience, there has been significant interest in creating assistive technologies to improve the quality of life of individuals Abstract Background: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. Methods: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Results: Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetra-plegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.
This paper presents an improvement of classification performance for electroencephalography (EEG)... more This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.
—Surface electromyography (sEMG) records muscle activities from the surface of muscles, which off... more —Surface electromyography (sEMG) records muscle activities from the surface of muscles, which offers a wealth of information concerning muscle activation patterns in both research and clinical settings. A key principle underlying sEMG analyses is the decomposition of the signal into a number of motor unit action potentials (MUAPs) that capture most of the relevant features embedded in a low-dimensional space. Toward this, the principal component analysis (PCA) has extensively been sought after, whereby the original sEMG data is translated into low-dimensional MUAP components with a reduced level of redundancy. The objective of this article is to disseminate the role of PCA in conjunction with quantitative sEMG analyses. Following the preliminaries on the sEMG methodology and a statement of PCA algorithm, an exhaustive collection of PCA applications related to sEMG data is in order. Alongside the technical challenges associated with the PCA-based sEMG processing, the envisaged research trend is also discussed. Index Terms—Surface electromyography (sEMG), artificial neural network (ANN), principal component analysis (PCA), motor unit action potential (MUAP), flexions, self-organizing feature map (SOFM), support vector regression (SVR), myoelectric signal.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, Jan 17, 2015
Surface electromyography (sEMG) based pattern recognition studies have been widely used to improv... more Surface electromyography (sEMG) based pattern recognition studies have been widely used to improve the classification accuracy of upper limb gestures. Information extracted from multiple sensors of the sEMG recording sites can be used as inputs to control powered upper limb prostheses. However, usage of multiple EMG sensors on the prosthetic hand is not practical and makes it difficult for amputees due to electrode shift/movement, and often amputees feel discomfort in wearing sEMG sensor array. Instead, using fewer numbers of sensors would greatly improve the controllability of prosthetic devices and it would add dexterity and flexibility in their operation. In this paper, we propose a novel myoelectric control technique for identification of various gestures using the minimum number of sensors based on Independent Component Analysis (ICA) and Icasso clustering. The proposed method is a model based approach where a combination of source separation and Icasso clustering was utilized ...
This study has investigated the relationship in the connectivity of motor units in surface electr... more This study has investigated the relationship in the connectivity of motor units in surface electromyogram (sEMG) of biceps brachii muscle. It is hypothesized that with ageing, there is reduction/loss in number of motor units, leading to reduction in the independence between the channels of the recorded muscle activity. Two channels of sEMG were recorded during three levels of isometric muscle contraction: 50 %, 75 % and 100 % maximal voluntary contraction (MVC). 73 subjects (age range 20-70) participated in the experiments. The independence in channel index (ICI) between the two sEMG recording locations was computed using the independent components and Frobenius norm. ANOVA Statistical analysis was performed to test the effect of age (loss of motor units) and level of contraction on ICI. The results show that the ICI among the older cohort was significantly lower compared with the younger adults. This research study has shown that the reduction in number of motor units is reflected by the reduction in the ICI of the sEMG signal.
— This paper presents a two-class electroencephalography (EEG)-based classification for classifyi... more — This paper presents a two-class electroencephalography (EEG)-based classification for classifying of driver fatigue (fatigue state vs. alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8% and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor) and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC=0.93) against other methods such as power spectral density (PSD) as feature extractor (AUC-ROC=0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications. Index Terms—electroencephalography (EEG), driver fatigue, autoregressive (AR) model, independent component analysis, entropy rate bound minimization, Bayesian neural network.
Surface electromyogram (sEMG) has numerous applications. It has been widely used in various biosi... more Surface electromyogram (sEMG) has numerous applications. It has been widely used in various biosignal and neuro rehabilitation applications. There is an urgent need for establishing a simple yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Earlier work to identify the hand actions and gestures based on sEMG suffers from limitation that these are suitable for gross actions where there is only one prime-mover muscle involved and not suitable for small subtle and complex muscle contraction. This paper presents the hand gesture identification using sEMG decomposed using semi-blind independent component analysis combined with neural network based classifier. The aim was to provide reliable and natural control for rehabilitation and human computer interaction applications. We have proposed a model based approach where the hand muscle anatomy is known. The system was tested on 5 subjects and with experiments repeated on different days. The system was compared with raw sEMG as used by other researchers. The system is able to classify the different hand actions 100%. In comparison, the classification of the traditional ICA and raw sEMG for the same experiments and similar features was a poor 65% and 60% respectively. This research demonstrates that sEMG can be decomposed to the individual muscle activities using semi-blind ICA. The muscle activity after decomposition can be used to accurately identify small and subtle hand actions and gestures. Finally the ICA source separation was validated with mixing matrix analysis.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, Jan 9, 2015
An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals... more An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a singlechannel EMG sensor. The ensemble empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the Fas...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014
Autism Spectrum Disorder (ASD) is a neural development disorder affecting the information process... more Autism Spectrum Disorder (ASD) is a neural development disorder affecting the information processing capability of the brain by altering how nerve cells and their synapses interconnect and organize. Electroencephalograph or EEG signals records the electrical activity of the brain from the scalp which can be utilized to identify and investigate the brain wave pattern which are specific to individuals with ASD. Therefore, the analysis of ASD can be done by scrutinizing the specific bands (Theta, Mu and Beta) of the EEG signal. However, EEG signals are mainly contaminated by Ocular (Eye-blink) and Myogenic artefacts which pose problems in EEG interpretation. In this paper an automated real-time method for detection and removal of Ocular and Myogenic artefacts for multichannel EEG signal is proposed which would enhance the diagnostic accuracy. The proposed methodology has been validated against 20 subjects from Caltech, Physionet, Swartz Center for Computational Neuroscience and the com...
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
The proposed signal processing technique uses Fast Independent Component Analysis (ICA) algorithm... more The proposed signal processing technique uses Fast Independent Component Analysis (ICA) algorithm to improve the quality of the original biosensors recordings, which can be used as valuable pre-processing technique such as cross talk removal, artefact reduction etc. Initially, the ill conditioned original surface Electromyography (sEMG) recordings were separated using ICA methods and later they were reconstructed using modified un-mixing matrix. The simulation results showed huge improvement of the original recorded signal after reconstruction. The proposed method has potential applications in various biomedical signal processing techniques.
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
Single Channel Blind Source Separation (SCBSS) is an extreme case of underdetermined (more source... more Single Channel Blind Source Separation (SCBSS) is an extreme case of underdetermined (more sources and fewer sensors) Blind Source Separation (BSS) problem. In this paper, we propose a novel technique using Local Mean Decomposition (LMD) and Independent Component Analysis (ICA) combined with single channel BSS (LMD_ICA). First, the LMD was used to decompose the single channel source into a series of data sequences, which are called as Product Functions (PF), then, ICA algorithm was used to process PFs to get similar independent components and extract the original signals. A comparison was made between LMD_ICA and previously proposed single channel ICA method (EEMD_ICA). The real time experimental results demonstrated the advantage of the proposed single channel source separation method for artifact removal and in biomedical source separation applications.
ABSTRACT Blind source separation (BSS) methods have received extensive attention over the past tw... more ABSTRACT Blind source separation (BSS) methods have received extensive attention over the past two decades; thanks to its wide applicability in a number of areas such as biomedical engineering, audio signal processing, and telecommunications. The problem of source separation is an inductive inference problem, as only limited information, e.g., the sensor observations, is available to infer the most probable source estimates. The aim of BSS is to process these observations (acquired by sensors or sensor arrays) in such a way that the original unknown source signals are extracted by, e.g., an adaptive system, or separated simultaneously using, e.g., a block (or batch)-based algorithm, without knowing or with very limited information about the characteristics of the transmission channels through which the sources propagate to the sensors. This book aims to disseminate timely to the scientific community the new developments in BSS spanning from theoretical frameworks, algorithmic developments, to a variety of applications. The book covers some emerging techniques in BSS, especially those developed recently, offering academic researchers and practitioners a comprehensive update about the new development in this field. The book provides a forum for researchers to exchange their ideas, and to foster a better understanding of the state of the art of the subject. We envisage that the publication of this book will motivate new ideas and more cutting-edge research activities in this area.
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, 2010
Classification of surface electromyogram (sEMG) signal is important for various applications such... more Classification of surface electromyogram (sEMG) signal is important for various applications such as prosthetic control and human computer interface. Surface EMG provides a better insight into the strength of muscle contraction which can be used as control signal for different applications. Due to the various interference between different muscle activities, it is difficult to identify movements using sEMG during low-level flexions. A new set of fractal features - fractal dimension and Maximum fractal length of sEMG has been previously reported by the authors. These features measure the complexity and strength of the muscle contraction during the low-level finger flexions. In order to classify and identify the low-level finger flexions using these features based on the fractal properties, a recently developed machine learning based classifier, Twin Support vector machines (TSVM) has been proposed. TSVM works on basic learning methodology and solves the classification tasks as two SVMs for each classes. This paper reports the novel method on the machine learning based classification of fractal features of sEMG using the Twin Support vector machines. The training and testing was performed using two different kernel functions - Linear and Radial Basis Function (RBF).
Background The new emerging frontier of technology modeling, cyber-physical systems (CPS), is rel... more Background The new emerging frontier of technology modeling, cyber-physical systems (CPS), is related to the generation of new systems with the integration of the physical systems and real-time computational intelligence capabilities. This creates a fully intelligent system to tackle a variety of application fields such as robotics, intelligent buildings, biomedical and healthcare, energy, manufacturing control systems, and avionics [1, 2]. In biomedical CPS fields, especially in cognitive neuroscience, there has been significant interest in creating assistive technologies to improve the quality of life of individuals Abstract Background: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. Methods: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Results: Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetra-plegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.
This paper presents an improvement of classification performance for electroencephalography (EEG)... more This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.
—Surface electromyography (sEMG) records muscle activities from the surface of muscles, which off... more —Surface electromyography (sEMG) records muscle activities from the surface of muscles, which offers a wealth of information concerning muscle activation patterns in both research and clinical settings. A key principle underlying sEMG analyses is the decomposition of the signal into a number of motor unit action potentials (MUAPs) that capture most of the relevant features embedded in a low-dimensional space. Toward this, the principal component analysis (PCA) has extensively been sought after, whereby the original sEMG data is translated into low-dimensional MUAP components with a reduced level of redundancy. The objective of this article is to disseminate the role of PCA in conjunction with quantitative sEMG analyses. Following the preliminaries on the sEMG methodology and a statement of PCA algorithm, an exhaustive collection of PCA applications related to sEMG data is in order. Alongside the technical challenges associated with the PCA-based sEMG processing, the envisaged research trend is also discussed. Index Terms—Surface electromyography (sEMG), artificial neural network (ANN), principal component analysis (PCA), motor unit action potential (MUAP), flexions, self-organizing feature map (SOFM), support vector regression (SVR), myoelectric signal.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, Jan 17, 2015
Surface electromyography (sEMG) based pattern recognition studies have been widely used to improv... more Surface electromyography (sEMG) based pattern recognition studies have been widely used to improve the classification accuracy of upper limb gestures. Information extracted from multiple sensors of the sEMG recording sites can be used as inputs to control powered upper limb prostheses. However, usage of multiple EMG sensors on the prosthetic hand is not practical and makes it difficult for amputees due to electrode shift/movement, and often amputees feel discomfort in wearing sEMG sensor array. Instead, using fewer numbers of sensors would greatly improve the controllability of prosthetic devices and it would add dexterity and flexibility in their operation. In this paper, we propose a novel myoelectric control technique for identification of various gestures using the minimum number of sensors based on Independent Component Analysis (ICA) and Icasso clustering. The proposed method is a model based approach where a combination of source separation and Icasso clustering was utilized ...
This study has investigated the relationship in the connectivity of motor units in surface electr... more This study has investigated the relationship in the connectivity of motor units in surface electromyogram (sEMG) of biceps brachii muscle. It is hypothesized that with ageing, there is reduction/loss in number of motor units, leading to reduction in the independence between the channels of the recorded muscle activity. Two channels of sEMG were recorded during three levels of isometric muscle contraction: 50 %, 75 % and 100 % maximal voluntary contraction (MVC). 73 subjects (age range 20-70) participated in the experiments. The independence in channel index (ICI) between the two sEMG recording locations was computed using the independent components and Frobenius norm. ANOVA Statistical analysis was performed to test the effect of age (loss of motor units) and level of contraction on ICI. The results show that the ICI among the older cohort was significantly lower compared with the younger adults. This research study has shown that the reduction in number of motor units is reflected by the reduction in the ICI of the sEMG signal.
— This paper presents a two-class electroencephalography (EEG)-based classification for classifyi... more — This paper presents a two-class electroencephalography (EEG)-based classification for classifying of driver fatigue (fatigue state vs. alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8% and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor) and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC=0.93) against other methods such as power spectral density (PSD) as feature extractor (AUC-ROC=0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications. Index Terms—electroencephalography (EEG), driver fatigue, autoregressive (AR) model, independent component analysis, entropy rate bound minimization, Bayesian neural network.
Surface electromyogram (sEMG) has numerous applications. It has been widely used in various biosi... more Surface electromyogram (sEMG) has numerous applications. It has been widely used in various biosignal and neuro rehabilitation applications. There is an urgent need for establishing a simple yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Earlier work to identify the hand actions and gestures based on sEMG suffers from limitation that these are suitable for gross actions where there is only one prime-mover muscle involved and not suitable for small subtle and complex muscle contraction. This paper presents the hand gesture identification using sEMG decomposed using semi-blind independent component analysis combined with neural network based classifier. The aim was to provide reliable and natural control for rehabilitation and human computer interaction applications. We have proposed a model based approach where the hand muscle anatomy is known. The system was tested on 5 subjects and with experiments repeated on different days. The system was compared with raw sEMG as used by other researchers. The system is able to classify the different hand actions 100%. In comparison, the classification of the traditional ICA and raw sEMG for the same experiments and similar features was a poor 65% and 60% respectively. This research demonstrates that sEMG can be decomposed to the individual muscle activities using semi-blind ICA. The muscle activity after decomposition can be used to accurately identify small and subtle hand actions and gestures. Finally the ICA source separation was validated with mixing matrix analysis.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, Jan 9, 2015
An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals... more An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a singlechannel EMG sensor. The ensemble empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the Fas...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014
Autism Spectrum Disorder (ASD) is a neural development disorder affecting the information process... more Autism Spectrum Disorder (ASD) is a neural development disorder affecting the information processing capability of the brain by altering how nerve cells and their synapses interconnect and organize. Electroencephalograph or EEG signals records the electrical activity of the brain from the scalp which can be utilized to identify and investigate the brain wave pattern which are specific to individuals with ASD. Therefore, the analysis of ASD can be done by scrutinizing the specific bands (Theta, Mu and Beta) of the EEG signal. However, EEG signals are mainly contaminated by Ocular (Eye-blink) and Myogenic artefacts which pose problems in EEG interpretation. In this paper an automated real-time method for detection and removal of Ocular and Myogenic artefacts for multichannel EEG signal is proposed which would enhance the diagnostic accuracy. The proposed methodology has been validated against 20 subjects from Caltech, Physionet, Swartz Center for Computational Neuroscience and the com...
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
The proposed signal processing technique uses Fast Independent Component Analysis (ICA) algorithm... more The proposed signal processing technique uses Fast Independent Component Analysis (ICA) algorithm to improve the quality of the original biosensors recordings, which can be used as valuable pre-processing technique such as cross talk removal, artefact reduction etc. Initially, the ill conditioned original surface Electromyography (sEMG) recordings were separated using ICA methods and later they were reconstructed using modified un-mixing matrix. The simulation results showed huge improvement of the original recorded signal after reconstruction. The proposed method has potential applications in various biomedical signal processing techniques.
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
Single Channel Blind Source Separation (SCBSS) is an extreme case of underdetermined (more source... more Single Channel Blind Source Separation (SCBSS) is an extreme case of underdetermined (more sources and fewer sensors) Blind Source Separation (BSS) problem. In this paper, we propose a novel technique using Local Mean Decomposition (LMD) and Independent Component Analysis (ICA) combined with single channel BSS (LMD_ICA). First, the LMD was used to decompose the single channel source into a series of data sequences, which are called as Product Functions (PF), then, ICA algorithm was used to process PFs to get similar independent components and extract the original signals. A comparison was made between LMD_ICA and previously proposed single channel ICA method (EEMD_ICA). The real time experimental results demonstrated the advantage of the proposed single channel source separation method for artifact removal and in biomedical source separation applications.
ABSTRACT Blind source separation (BSS) methods have received extensive attention over the past tw... more ABSTRACT Blind source separation (BSS) methods have received extensive attention over the past two decades; thanks to its wide applicability in a number of areas such as biomedical engineering, audio signal processing, and telecommunications. The problem of source separation is an inductive inference problem, as only limited information, e.g., the sensor observations, is available to infer the most probable source estimates. The aim of BSS is to process these observations (acquired by sensors or sensor arrays) in such a way that the original unknown source signals are extracted by, e.g., an adaptive system, or separated simultaneously using, e.g., a block (or batch)-based algorithm, without knowing or with very limited information about the characteristics of the transmission channels through which the sources propagate to the sensors. This book aims to disseminate timely to the scientific community the new developments in BSS spanning from theoretical frameworks, algorithmic developments, to a variety of applications. The book covers some emerging techniques in BSS, especially those developed recently, offering academic researchers and practitioners a comprehensive update about the new development in this field. The book provides a forum for researchers to exchange their ideas, and to foster a better understanding of the state of the art of the subject. We envisage that the publication of this book will motivate new ideas and more cutting-edge research activities in this area.
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, 2010
Classification of surface electromyogram (sEMG) signal is important for various applications such... more Classification of surface electromyogram (sEMG) signal is important for various applications such as prosthetic control and human computer interface. Surface EMG provides a better insight into the strength of muscle contraction which can be used as control signal for different applications. Due to the various interference between different muscle activities, it is difficult to identify movements using sEMG during low-level flexions. A new set of fractal features - fractal dimension and Maximum fractal length of sEMG has been previously reported by the authors. These features measure the complexity and strength of the muscle contraction during the low-level finger flexions. In order to classify and identify the low-level finger flexions using these features based on the fractal properties, a recently developed machine learning based classifier, Twin Support vector machines (TSVM) has been proposed. TSVM works on basic learning methodology and solves the classification tasks as two SVMs for each classes. This paper reports the novel method on the machine learning based classification of fractal features of sEMG using the Twin Support vector machines. The training and testing was performed using two different kernel functions - Linear and Radial Basis Function (RBF).
Biological engineering is a field of engineering in which the emphasis is on life and life-sustai... more Biological engineering is a field of engineering in which the emphasis is on life and life-sustaining systems. Biological engineering is an emerging discipline that encompasses engineering theory and practice connected to and derived from the science of biology. The most important trend in biological engineering is the dynamic range of scales at which biotechnology is now able to integrate with biological processes. An explosion in micro/nanoscale technology is allowing the manufacture of nanoparticles for drug delivery into cells, miniaturized implantable microsensors for medical diagnostics, and micro-engineered robots for on-board tissue repairs. This book aims to provide an updated overview of the recent developments in biological engineering from diverse aspects and various applications in clinical and experimental research.
This book is intended for both mechanical and electronics engineers (researchers and graduate stu... more This book is intended for both mechanical and electronics engineers (researchers and graduate students) who wish to get some training in smart electronics devices embedded in mechanical systems. The book is partly a textbook and partly a monograph. It is a textbook as it provides a focused interdisciplinary experience for undergraduates that encompass important elements from traditional courses as well as contemporary developments in Mechatronics. It is simultaneously a monograph because it presents several new results and ideas and further developments and explanation of existing algorithms which are brought together and published in the book for the first time.
Independent Component Analysis (ICA) is a signal-processing method to extract independent sources... more Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image processing, telecommunications, medical signal processing and several data mining issues. This book brings the state-of-the-art of some of the most important current research of ICA related to Audio and Biomedical signal processing applications. The book is partly a textbook and partly a monograph. It is a textbook because it gives a detailed introduction to ICA applications. It is simultaneously a monograph because it presents several new results, concepts and further developments, which are brought together and published in the book.
Electromyography (EMG) is a technique for evaluating and recording the electrical activity produc... more Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research.
Electromyography (EMG) is a procedure for assessing and recording the electrical activity produce... more Electromyography (EMG) is a procedure for assessing and recording the electrical activity produced by skeletal muscles. Since the contracting skeletal muscles are greatly responsible for loading the bones and joints, information about the muscle EMG is important to gain knowledge about muscular-skeletal biomechanics.
Applications, Challenges, and Advancements in Electromyography Signal Processing provides an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. Presenting new results, concepts, and further developments in the field of EMG signal processing, this publication is an ideal resource for graduate and post-graduate students, academicians, engineers, and scientists in the fields of signal processing and biomedical engineering.
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Applications, Challenges, and Advancements in Electromyography Signal Processing provides an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. Presenting new results, concepts, and further developments in the field of EMG signal processing, this publication is an ideal resource for graduate and post-graduate students, academicians, engineers, and scientists in the fields of signal processing and biomedical engineering.