Blind Source Separation Methods Applied to Muscle Artefacts Removing from Epileptic Eeg Recording: A Comparative Study.

International audienceElectroencephalogram (EEG) recordings are often contaminated with muscle artifacts. These artifacts obscure the EEG and complicate its interpretation or even make the interpretation unfeasible. In this paper, realistic spike EEG signals are simulated from the activation of a 5 cm2 epileptic patch in the left superior temporal gyrus. Background activities and real muscle artifacts are then added to the simulated data. We compare the efficiency of Empirical Mode Decomposition (EMD), Independent Component Analysis (ICA) and Blind Source Separation based on Canonical Correlation Analysis (BSS-CCA) to remove muscle artifacts from the EEG signals. The quantitative comparison indicates that the EMD approach exhibits a better performance than ICA and BSS-CCA, especially in the case of very low Signal to Noise Ratio (SNR)

An improved EEG pattern classification system based on dimensionality reduction and classifier fusion

University of Technology, Sydney. Faculty of Engineering and Information Technology.Analysis of brain electrical activities (Electroencephalography, EEG) presents a rich source of information that helps in the advancement of affordable and effective biomedical applications such as psychotropic drug research, sleep studies, seizure detection and brain computer interface (BCI). Interpretation and understanding of EEG signal will provide clinicians and physicians with useful information for disease diagnosis and monitoring biological activities. It will also help in creating a new way of communication through brain waves. This thesis aims to investigate new algorithms for improving pattern recognition systems in two main EEG-based applications. The first application represents a simple Brain Computer Interface (BCI) based on imagined motor tasks, whilst the second one represents an automatic sleep scoring system in intensive care unit. BCI system in general aims to create a lion-muscular link between brain and external devices, thus providing a new control scheme that can most benefit the extremely immobilised persons. This link is created by utilizing pattern recognition approach to interpret EEG into device commands. The commands can then be used to control wheelchairs, computers or any other equipment. The second application relates to creating an automatic scoring system through interpreting certain properties of several biomedical signals. Traditionally, sleep specialists record and analyse brain signal using electroencephalogram (EEG), muscle tone (EMG), eye movement (EOG), and other biomedical signals to detect five sleep stages: Rapid Eye Movement (REM), stage 1,... to stage 4. Acquired signals are then scored based on 30 seconds intervals that require manually inspecting one segment at a time for certain properties to interpret sleep stages. The process is time consuming and demands competence. It is thought that an automatic scoring system mimicking sleep expert rules will speed up the process and reduce the cost. Practicality of any EEG-based system depends upon accuracy and speed. The more accurate and faster classification systems are, the better will be the chance to integrate them in wider range of applications. Thus, the performance of the previous systems is further enhanced using improved feature selection, projection and classification algorithms. As processing EEG signals requires dealing with multi-dimensional data, there is a need to minimize the dimensionality in order to achieve acceptable performance with less computational cost. The first possible candidate for dimensionality reduction is employed using channel feature selection approach. Four novel feature selection methods are developed utilizing genetic algorithms, ant colony, particle swarm and differential evolution optimization. The methods provide fast and accurate implementation in selecting the most informative features/channels that best represent mental tasks. Thus, computational burden of the classifier is kept as light as possible by removing irrelevant and highly redundant features. As an alternative to dimensionality reduction approach, a novel feature projection method is also introduced. The method maps the original feature set into a small informative subset of features that can best discriminate between the different class. Unlike most existing methods based on discriminant analysis, the proposed method considers fuzzy nature of input measurements in discovering the local manifold structure. It is able to find a projection that can maximize the margin between data points from different classes at each local area while considering the fuzzy nature. In classification phase, a number of improvements to traditional nearest neighbour classifier (kNN) are introduced. The improvements address kNN weighting scheme limitations. The traditional kNN does not take into account class distribution, importance of each feature, contribution of each neighbour, and the number of instances for each class. The proposed kNN variants are based on improved distance measure and weight optimization using differential evolution. Differential evolution optimizer is utilized to enhance kNN performance through optimizing the metric weights of features, neighbours and classes. Additionally, a Fuzzy kNN variant has also been developed to favour classification of certain classes. This variant may find use in medical examination. An alternative classifier fusion method is introduced that aims to create a set of diverse neural network ensemble. The diversity is enhanced by altering the target output of each network to create a certain amount of bias towards each class. This enables the construction of a set of neural network classifiers that complement each other

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Electromyography and ultrasonography provide complementary information about electrophysiological and physical (i.e. anatomical and mechanical) muscle properties. In this study, we propose a method to assess the electrical and physical properties of single motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted from HDsEMG were used to identify the corresponding region of muscle tissue displacement in US videos. The time evolution of the tissue velocity in the identified region was regarded as the MU tissue displacement velocity. The method was tested in simulated conditions and applied to experimental signals to study the local association between the amplitude distribution of single MU action potentials and the identified displacement area. We were able to identify the location of simulated MUs in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU displacement velocity (cc > 0.85). Multiple regression analysis of 180 experimental MUs detected during isometric contractions of the biceps brachii revealed a significant association between the identified location of MU displacement areas and the centroid of the EMG amplitude distribution. The proposed approach has the potential to enable non-invasive assessment of the electrical, anatomical, and mechanical properties of single MUs in voluntary contractions

A denoising algorithm for surface EMG decomposition

The goal of the present thesis was to investigate a novel motor unit potential train (MUPT) editing routine, based on decreasing the variability in shape (variance ratio, VR) of the MUP ensemble. Decomposed sEMG data from 20 participants at 60% MVC of wrist flexion was used. There were two levels of denoising (relaxed and strict) criteria for removing discharge times associated with waveforms that did not decrease the VR and increase its signal-to-noise ratio (SNR) of the MUP ensemble. The peak-to-peak amplitude and the duration between the positive and negative peaks for the MUP template were dependent on the level of denoising (p’s 0.05). The same was true between denoising criteria (p>0.05). Editing the MUPT based on MUP shape resulted in significant differences in measures extracted from the MUP template, with trivial difference between the standard error of estimate for mean IDIs between the complete and denoised MUPTs

Multichannel surface EMG decomposition based on measurement correlation and LMMSE

A method based on measurement correlation (MC) and linear minimum mean square error (LMMSE) for multichannel surface electromyography (sEMG) signal decomposition was developed in this study. This MC-LMMSE method gradually and iteratively increases the correlation between an optimized vector and a reconstructed matrix that is correlated with the measurement matrix. The performance of the proposed MC-LMMSE method was evaluated with both simulated and experimental sEMG signals. Simulation results show that the MC-LMMSE method can successfully reconstruct up to 53 innervation pulse trains with a true positive rate greater than 95%. The performance of the MC-LMMSE method was also evaluated using experimental sEMG signals collected with a 64-channel electrode array from the first dorsal interosseous muscles of three subjects at different contraction levels. A maximum of 16 motor units were successfully extracted from these multichannel experimental sEMG signals. The performance of the MC-LMMSE method was further evaluated with multichannel experimental sEMG data by using the “two sources” method. The large population of common MUs extracted from the two independent subgroups of sEMG signals demonstrates the reliability of the MC-LMMSE method in multichannel sEMG decomposition

Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram

Funding Information: For Arnaldo Batista and Manuel Ortigueira, this work was supported by the Portuguese National Funds, through the FCT Foundation for Science and Technology, within the scope of the CTS Research Unit, Center of Technology and Systems, UNINOVA, under the project UIDB/00066/2020 (FCT). Helena Mouriño was financed by national funds through FCT, Fundação para a Ciência e a Tecnologia, under the project UIDB/00006/2020. Publisher Copyright: © 2022 by the authors.The electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be −16.74%, −20.32%, and −15.78%, respectively (p-value = 1.31 × 10−12).publishersversionpublishe

Recursive decomposition of electromyographic signals with a varying number of active sources: Bayesian modelling and filtering

International audienceThis paper describes a sequential decomposition algorithm for single channel intramuscular electromyography (iEMG) generated by a varying number of active motor neurons. As in previous work, we establish a Hidden Markov Model of iEMG, in which each motor neuron spike train is modeled as a renewal process with inter-spike intervals following a discrete Weibull law and motor unit action potentials are modeled as impulse responses of linear time-invariant systems with known prior. We then expand this model by introducing an activation vector associated to the state vector of the Hidden Markov Model. This activation vector represents recruitment/derecruitment of motor units and is estimated together with the state vector using Bayesian filtering. Non-stationarity of the model parameters is addressed by means of a sliding window approach, thus making the algorithm adaptive to variations in contraction force and motor unit action potential waveforms. The algorithm was validated using simulated and experimental iEMG signals with varying number of active motor units. The experimental signals were acquired from the tibialis anterior and abductor digiti minimi muscles by fine wire and needle electrodes. The decomposition accuracy in both simulated and experimental signals exceeded 90% and the recruitment/derecruitment was successfully tracked by the algorithm. Because of its parallel structure, this algorithm can be efficiently accelerated, which lays the basis for its future real-time applications in human-machine interfaces, e.g. for prosthetic control

Motor Unit Spike Trains Recontruction from sEMG Signals for Gesture Classification on a Low-Power Processing Platform

Hand gesture recognition based on surface electromyography (sEMG) signals is a promising approach for the development of intuitive human-machine interfaces (HMIs) in domains such as robotics and prosthetics. The sEMG signal arises from the muscles' electrical activity, and can thus be used to recognize hand gestures. The decoding from sEMG signals to actual control signals is non-trivial; typically, control systems map sEMG patterns into a set of gestures using machine learning, failing to incorporate any physiological insight. This master thesis aims at developing a bio-inspired hand gesture recognition system based on neuromuscular spike extraction rather than on simple pattern recognition. The system relies on a decomposition algorithm based on independent component analysis (ICA) that decomposes the sEMG signal into its constituent motor unit spike trains, which are then forwarded to a machine learning classifier. Since ICA does not guarantee a consistent motor unit ordering across different sessions, 3 approaches are proposed: 2 ordering criteria based on firing rate and negative entropy, and a re-calibration approach that allows the decomposition model to retain information about previous sessions. Using a multilayer perceptron (MLP), the latter approach results in an accuracy up to 99.4% in a 1-subject, 1-degree of freedom scenario. Afterwards, the decomposition and classification pipeline for inference is parallelized and profiled on the PULP platform, achieving a latency 92% in a 1-subject, 5-classes (4 gestures and rest) scenario. These results prove that the proposed system is suitable for real-time execution on embedded platforms and also capable of matching the accuracy of state-of-the-art approaches, while also giving some physiological insight on the neuromuscular spikes underlying the sEMG