—This paper presents a comprehensive study on the analysis of neuromuscular signal activities to ... more —This paper presents a comprehensive study on the analysis of neuromuscular signal activities to recognize eleven facial expressions for Muscle Computer Interfacing applications. A robust denoising protocol comprised of Wavelet transform and Kalman filtering is proposed to enhance the electromyogram (EMG) signal-to-noise ratio and improve classification performance. The effectiveness of eight different time-domain facial EMG features on system performance is examined and compared in order to identify the most discriminative one. Fourteen pattern recognition-based algorithms are employed to classify the extracted features. These classifiers are evaluated in terms of classification accuracy and processing time. Finally, the best methods that obtain almost identical system performance are compared through the Normalized Mutual Information (NMI) criterion and a repeated measure analysis of variance (ANOVA) for a statistical significant test.To clarify the impact of signal denoising, all considered EMG features and classifiers are assessed with and without this stage. Results show that: (1) the proposed denosing step significantly improves the system performance; (2) Root Mean Square is the most discriminative facial EMG feature; (3) discriminant analysis when the parameters are estimated by the Maximum Likelihood algorithm achieves the highest classification accuracy and NMI; however, ANOVA reveals no significant difference among the best methods with almost similar performance.
—Electromyogram (EMG)-based facial gesture recognition has recently drawn the researchers' attent... more —Electromyogram (EMG)-based facial gesture recognition has recently drawn the researchers' attention as a potential medium in different areas, particularly in assistive technology and rehabilitation. Efficient analysis of facial neuromuscular signals generated by different facial muscles can provide lots of information about underlying facial movement mechanisms which can be used to characterize different facial gestures as well as muscle abnormalities like in patients with muscular dystrophy or facial palsy. This paper investigated time-varying properties of facial EMGs in time-frequency domain. Significantly we studied changes of EMG spectrum across time while performing ten different facial gestures. The facial gestures were recorded from ten individuals through three bipolar pairs of surface electrodes. Time-Frequency analysis was carried out using B-Distribution to resolve EMG components in time-frequency domain and specify the signal frequency components that change over time. We observed that 1) there were no significant differences among facial gestures EMG time-varying spectrum distributions, 2) EMG power spectrum decreased over time in each epoch after about one second from the beginning of each movement, 3) the most significant power spectrum of facial EMGs was within 60-300 Hz.
— Performance of motor imagery (MI)-based brain computer interfaces (BCIs) highly depends on the ... more — Performance of motor imagery (MI)-based brain computer interfaces (BCIs) highly depends on the extraction of features from different brain neural processes. Commonly designed BCIs use band power changes in single channel electroencephalograms (EEGs) to discriminate different MI tasks. In this paper, we studied the information about interactions of spatially separated brain areas by considering the relationships between brain source signals through functional connectivity. We investigated dynamic time-frequency connectivity patterns during four motor imagery movements including left hand, right hand, both feet and tongue by means of Coherence measure estimated from multivariate adaptive autoregressive model coefficients. To tackle the volume conduction problem, sensor space signals were transformed into source space using independent component analysis technique. We showed distinct time-varying Coherence for different motor imageries. For tongue MI movement, bilateral connectivity was observed in hand areas of both hemispheres. The similar connectivity was detected for feet MI task, but with more focus on right hemisphere. During right and left hand MI, bilateral and contralateral brain connectivities were observed respectively. Results provided valuable information for possible classification of MI tasks by considering brain source functional connectivity patterns for BCIs.
Recent research has reached a consensus on the feasibility of motor imagery brain-computer interf... more Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic (EEG) sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.
Developing efficient and usable brain-computer interfaces (BCIs) requires well-designed trade-off... more Developing efficient and usable brain-computer interfaces (BCIs) requires well-designed trade-off between accuracy and computational time. This paper presents a very fast and accurate method to classify asynchronous brain signals from a multi-class mental tasks dataset using time-domain features. Five different statistical time-domain features were extracted to characterize various properties of three mental tasks electroencephalograms (EEGs). Versatile Elliptic Basis Function Neural Network (VEBFNN) was employed to classify single EEG features as well as multi-feature set. Discriminating power of single features was evaluated and compared by considering the classification accuracy and computational cost consumed during the training stage. Finally, the performance of the best single EEG feature was compared to the multi-feature set. The results indicated the usefulness of Willison Amplitude EEG feature in classifying the different motor tasks as it provided the highest discriminatio...
Inter-channel time-varying (TV) relationships of
scalp neural recordings offer deep understanding... more Inter-channel time-varying (TV) relationships of scalp neural recordings offer deep understanding of the brain sensory and cognitive functions. This paper develops a state space-based TV multivariate autoregressive (MVAR) model for estimating TV-information flow (IF) recruited by different motor imagery (MI) movements. TV model coefficients are computed through Kalman filter (KF) by incorporating Kalman smoothing approach and expectation-maximization algorithm for model parameter estimation, KS-EM. Volume conduction (VC) problem is also addressed by considering full noise covariate in observation equation. An automated model initialization is also implemented to deliver optimal estimates. TV-partial directed coherence derived from the proposed model is applied for IF analysis. The performance of KS-EM is assessed and compared with dual extended KF and overlapping sliding window-based MVAR models using simulated data. Finally, TV-IF during four different MI movements is studied. Results show the superiority of KS-EM for tracking the rapid signal parameter changes and eliminating the VC effect in the sensor space EEG. Differences in contralateral/ipsilateral TV-IF around alpha and lower beta bands during each MI task reveal the high potential of this feature for BCI applications.
Facial neuromuscular signal has recently drawn the researchers’ attention to its outstanding pote... more Facial neuromuscular signal has recently drawn the researchers’ attention to its outstanding potential as an efficient medium for Muscle Computer Interface (MuCI) applications. The proper analysis of such electromyogram (EMG) signals is essential in designing the interfaces. In this article, a multiclass least-square support vector machine (LS-SVM) is proposed for classification of different facial gestures EMG signals. EMG signals were captured through three bi-polar electrodes from ten participants while gesturing ten different facial states. EMGs were filtered and segmented into non-overlapped windows from which root mean square (RMS) features were extracted and then fed to the classifier. For the purpose of classification, different models of LS-SVM were constructed while tuning the kernel parameters automatically and manually. In the automatic mode, 48 models were formed while parameters of linear and radial basis function (RBF) kernels were tuned using different optimization techniques, cost functions and encoding schemes. In the manual mode, 8 models were shaped by means of the considered kernel functions and encoding schemes. In order to find the best model with a reliable performance, constructed models were evaluated and compared in terms of classification accuracy and computational cost. Results reported that the model including RBF kernel which was tuned manually and encoded by one-versus-all scheme provided the highest classification accuracy (93.10%) and consumed 0.98 s for training. It was indicated that automatic models were outperformed since they required too much time for tuning the parameters without any meaningful improvement in the final classification accuracy. The robustness of the selected LS-SVM model was evaluated through comparison with Support Vector Machine, fuzzy C-Means and fuzzy Gath-Geva clustering techniques.
This paper considers identifying effective cortical connectivity
from scalp EEG. Recent studies u... more This paper considers identifying effective cortical connectivity from scalp EEG. Recent studies use time-varying multivariate autoregressive (TV-MAR) models to better describe the changing connectivity between cortical regions where the TV coefficients are estimated by Kalman filter (KF) within a state-space framework. We extend this approach by incorporating Kalman smoothing (KS) to improve the KF estimates, and the expectation-maximization (EM) algorithm to infer the unknown model parameters from EEG. We also consider solving the volume conduction problem by modeling the induced instantaneous correlations using a full noise covariate. Simulation results show the superiority of KS in tracking the coefficient changes. We apply two derived frequency domain measures i.e. TV partial directed coherence (TV-PDC) and TV directed transfer function (TV-DTF), to investigate dynamic causal interactions between motor areas in discriminating motor imagery (MI) of left and right hand. Event-related changes of information flows around beta-band, in a unidirectional way between left and right hemispheres are observed during MI. A difference in interhemispheric connectivity patterns is found between left and righthand movements, implying potential usage for BCI.
Many studies have reported the usefulness of motor
imagery (MI) electroencephalogram (EEG) signal... more Many studies have reported the usefulness of motor imagery (MI) electroencephalogram (EEG) signals for Brain Computer Interface (BCI) systems. MI has been broadly characterized by the average of event-related changes of brain activity at specific frequency bands; but, temporal features of EEG have rarely been considered to identify different mental states of BCIs’ users. Additionally, complex classification techniques may have been proposed to enhance the accuracy of system but they may cause a notable delay during online applications. This paper investigated the application of neural network-based algorithms to classify three-class MIs by utilizing EEG time-domain features. Integrated EEG (IEEG) and Root Mean Square (RMS) features were extracted from EEG signals. Then, Multilayer Perceptron and Radial Basis Function Neural Networks were employed to classify the features. The discrimination ratio of such features were examined and compared through different classifiers. Moreover, the robustness of classifiers was investigated and compared. The results of this study indicated that RMS was more capable than IEEG for characterizing MI movements and RBF was more accurate and faster than MLP. The effectiveness of IEEG and RMS features and the performance of MLP and RBF classifiers were compared with Willison Amplitude (WAMP) feature and support vector machine (SVM) classifier respectively. This study proved that WAMP and SVM were more efficient for classification of MI tasks in both terms of accuracy (88.96%) and training time (0.5 second); however, considerable difference was not observed since RBF performed as fast as SVM with only about 3% less accuracy.
Background
This study proposes cross-match technique to detect the
presence of heart murmur in ph... more Background This study proposes cross-match technique to detect the presence of heart murmur in phonocardiogram. The sample data were recorded using an electronic stethoscope from real patient who suffers from structural heart disease such as Mitral Stenosis/Regurgitation and Aortic Stenosis/Regurgitation. The disease is confirmed by cardiologist with the support from an echocardiogram machine. Method The data were segmented into cardiac cycles manually. Each cycle is carefully observed to ensure that only clean data is accepted for the experiment. The features were extracted using Instantaneous Energy and Frequency (IEFE) method. The selected features reflect the sound originated from the structural defects or physical malfunction of the heart mechanics during the blood pumping activities. Cross- Match technique was then applied to train the input features to build the model barcode. Result The performance using the proposed method provides an accuracy of 86.4% which is comparable to the performance of other classifier such as support vector machine and neural network using the same experimental datasets. Conclusion It is concluded that cross-match performance is comparable with other classifier such as support vector machine and neural network. As it is very low in complexity and fast in computational time, is has a great potential to be used as heart murmur detection tool in medical facility such as primary care center.
This paper compared the application of multilayer perceptron (MLP) and radial basis function (RBF... more This paper compared the application of multilayer perceptron (MLP) and radial basis function (RBF) neural networks on a facial gesture recognition system. Electromyogram (EMG) signals generated by ten different facial gestures were recorded through three pairs of electrodes. EMGs were filtered and segmented into non-overlapped portions. The time-domain feature mean absolute value (MAV) and its two modified derivatives MMAV1 and MMAV2 were extracted. MLP and RBF were used to classify the EMG features while six types of activation functions were evaluated for MLP architecture. The discriminating power of single/multi features was also investigated. The results of this study showed that symmetric saturating linear was the most effective activation function for MLP; the feature set MAV + MMAV1 provided the highest accuracy by both classifiers; MLP reached higher recognition ratio for most of features; RBF was the faster algorithm which also offered a reliable trade-off between the two key metrics, accuracy and time.
Facial gesture recognition (FGR) is considered as a state-of-the-art which has drawn the research... more Facial gesture recognition (FGR) is considered as a state-of-the-art which has drawn the researchers’ attention in numerous fields of study due to its high potential in different applications. Recognizing the gestures through bio-signals generated from facial muscle movements has been recently proposed as an accurate and reliable pathway. The performance of gesture recognition-based systems directly depends on the effectiveness of classification techniques. Besides, a reasonable trade-off between recognition accuracy and computational cost is counted as the most significant factor for designing such systems. The aim of this paper was the classification of facial gestures electromyogram (EMG) signals by means of a least square support vector machine (LS-SVM) algorithm. Ten predefined facial gestures EMGs were recorded from ten participants through three bi-polar channels. Acquired signals were preprocessed using a band-pass filter and a segmentation technique. Then, time-domain features mean absolute value (MAV) and root mean square (RMS) were extracted from each segment. In order to classify the features, LS-SVM was implemented by considering radial basis function kernel and two multiclass encoding schemes, one-versus-one (OVO) and oneversus- all (OVA). This research showed that LS-SVM was a robust method for classification of facial gestures with 97.1% classification accuracy and 1.37 seconds training time when utilizing the feature combination MAV+RMS and the encoding technique OVA. It was also concluded that LS-SVM outperformed SVM and fuzzy c-means classifiers in this field of study. The results of this paper can be used as efficient processing tools in designing reliable interfaces for FGR systems.
—This paper presents a comprehensive study on the analysis of neuromuscular signal activities to ... more —This paper presents a comprehensive study on the analysis of neuromuscular signal activities to recognize eleven facial expressions for Muscle Computer Interfacing applications. A robust denoising protocol comprised of Wavelet transform and Kalman filtering is proposed to enhance the electromyogram (EMG) signal-to-noise ratio and improve classification performance. The effectiveness of eight different time-domain facial EMG features on system performance is examined and compared in order to identify the most discriminative one. Fourteen pattern recognition-based algorithms are employed to classify the extracted features. These classifiers are evaluated in terms of classification accuracy and processing time. Finally, the best methods that obtain almost identical system performance are compared through the Normalized Mutual Information (NMI) criterion and a repeated measure analysis of variance (ANOVA) for a statistical significant test.To clarify the impact of signal denoising, all considered EMG features and classifiers are assessed with and without this stage. Results show that: (1) the proposed denosing step significantly improves the system performance; (2) Root Mean Square is the most discriminative facial EMG feature; (3) discriminant analysis when the parameters are estimated by the Maximum Likelihood algorithm achieves the highest classification accuracy and NMI; however, ANOVA reveals no significant difference among the best methods with almost similar performance.
—Electromyogram (EMG)-based facial gesture recognition has recently drawn the researchers' attent... more —Electromyogram (EMG)-based facial gesture recognition has recently drawn the researchers' attention as a potential medium in different areas, particularly in assistive technology and rehabilitation. Efficient analysis of facial neuromuscular signals generated by different facial muscles can provide lots of information about underlying facial movement mechanisms which can be used to characterize different facial gestures as well as muscle abnormalities like in patients with muscular dystrophy or facial palsy. This paper investigated time-varying properties of facial EMGs in time-frequency domain. Significantly we studied changes of EMG spectrum across time while performing ten different facial gestures. The facial gestures were recorded from ten individuals through three bipolar pairs of surface electrodes. Time-Frequency analysis was carried out using B-Distribution to resolve EMG components in time-frequency domain and specify the signal frequency components that change over time. We observed that 1) there were no significant differences among facial gestures EMG time-varying spectrum distributions, 2) EMG power spectrum decreased over time in each epoch after about one second from the beginning of each movement, 3) the most significant power spectrum of facial EMGs was within 60-300 Hz.
— Performance of motor imagery (MI)-based brain computer interfaces (BCIs) highly depends on the ... more — Performance of motor imagery (MI)-based brain computer interfaces (BCIs) highly depends on the extraction of features from different brain neural processes. Commonly designed BCIs use band power changes in single channel electroencephalograms (EEGs) to discriminate different MI tasks. In this paper, we studied the information about interactions of spatially separated brain areas by considering the relationships between brain source signals through functional connectivity. We investigated dynamic time-frequency connectivity patterns during four motor imagery movements including left hand, right hand, both feet and tongue by means of Coherence measure estimated from multivariate adaptive autoregressive model coefficients. To tackle the volume conduction problem, sensor space signals were transformed into source space using independent component analysis technique. We showed distinct time-varying Coherence for different motor imageries. For tongue MI movement, bilateral connectivity was observed in hand areas of both hemispheres. The similar connectivity was detected for feet MI task, but with more focus on right hemisphere. During right and left hand MI, bilateral and contralateral brain connectivities were observed respectively. Results provided valuable information for possible classification of MI tasks by considering brain source functional connectivity patterns for BCIs.
Recent research has reached a consensus on the feasibility of motor imagery brain-computer interf... more Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic (EEG) sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.
Developing efficient and usable brain-computer interfaces (BCIs) requires well-designed trade-off... more Developing efficient and usable brain-computer interfaces (BCIs) requires well-designed trade-off between accuracy and computational time. This paper presents a very fast and accurate method to classify asynchronous brain signals from a multi-class mental tasks dataset using time-domain features. Five different statistical time-domain features were extracted to characterize various properties of three mental tasks electroencephalograms (EEGs). Versatile Elliptic Basis Function Neural Network (VEBFNN) was employed to classify single EEG features as well as multi-feature set. Discriminating power of single features was evaluated and compared by considering the classification accuracy and computational cost consumed during the training stage. Finally, the performance of the best single EEG feature was compared to the multi-feature set. The results indicated the usefulness of Willison Amplitude EEG feature in classifying the different motor tasks as it provided the highest discriminatio...
Inter-channel time-varying (TV) relationships of
scalp neural recordings offer deep understanding... more Inter-channel time-varying (TV) relationships of scalp neural recordings offer deep understanding of the brain sensory and cognitive functions. This paper develops a state space-based TV multivariate autoregressive (MVAR) model for estimating TV-information flow (IF) recruited by different motor imagery (MI) movements. TV model coefficients are computed through Kalman filter (KF) by incorporating Kalman smoothing approach and expectation-maximization algorithm for model parameter estimation, KS-EM. Volume conduction (VC) problem is also addressed by considering full noise covariate in observation equation. An automated model initialization is also implemented to deliver optimal estimates. TV-partial directed coherence derived from the proposed model is applied for IF analysis. The performance of KS-EM is assessed and compared with dual extended KF and overlapping sliding window-based MVAR models using simulated data. Finally, TV-IF during four different MI movements is studied. Results show the superiority of KS-EM for tracking the rapid signal parameter changes and eliminating the VC effect in the sensor space EEG. Differences in contralateral/ipsilateral TV-IF around alpha and lower beta bands during each MI task reveal the high potential of this feature for BCI applications.
Facial neuromuscular signal has recently drawn the researchers’ attention to its outstanding pote... more Facial neuromuscular signal has recently drawn the researchers’ attention to its outstanding potential as an efficient medium for Muscle Computer Interface (MuCI) applications. The proper analysis of such electromyogram (EMG) signals is essential in designing the interfaces. In this article, a multiclass least-square support vector machine (LS-SVM) is proposed for classification of different facial gestures EMG signals. EMG signals were captured through three bi-polar electrodes from ten participants while gesturing ten different facial states. EMGs were filtered and segmented into non-overlapped windows from which root mean square (RMS) features were extracted and then fed to the classifier. For the purpose of classification, different models of LS-SVM were constructed while tuning the kernel parameters automatically and manually. In the automatic mode, 48 models were formed while parameters of linear and radial basis function (RBF) kernels were tuned using different optimization techniques, cost functions and encoding schemes. In the manual mode, 8 models were shaped by means of the considered kernel functions and encoding schemes. In order to find the best model with a reliable performance, constructed models were evaluated and compared in terms of classification accuracy and computational cost. Results reported that the model including RBF kernel which was tuned manually and encoded by one-versus-all scheme provided the highest classification accuracy (93.10%) and consumed 0.98 s for training. It was indicated that automatic models were outperformed since they required too much time for tuning the parameters without any meaningful improvement in the final classification accuracy. The robustness of the selected LS-SVM model was evaluated through comparison with Support Vector Machine, fuzzy C-Means and fuzzy Gath-Geva clustering techniques.
This paper considers identifying effective cortical connectivity
from scalp EEG. Recent studies u... more This paper considers identifying effective cortical connectivity from scalp EEG. Recent studies use time-varying multivariate autoregressive (TV-MAR) models to better describe the changing connectivity between cortical regions where the TV coefficients are estimated by Kalman filter (KF) within a state-space framework. We extend this approach by incorporating Kalman smoothing (KS) to improve the KF estimates, and the expectation-maximization (EM) algorithm to infer the unknown model parameters from EEG. We also consider solving the volume conduction problem by modeling the induced instantaneous correlations using a full noise covariate. Simulation results show the superiority of KS in tracking the coefficient changes. We apply two derived frequency domain measures i.e. TV partial directed coherence (TV-PDC) and TV directed transfer function (TV-DTF), to investigate dynamic causal interactions between motor areas in discriminating motor imagery (MI) of left and right hand. Event-related changes of information flows around beta-band, in a unidirectional way between left and right hemispheres are observed during MI. A difference in interhemispheric connectivity patterns is found between left and righthand movements, implying potential usage for BCI.
Many studies have reported the usefulness of motor
imagery (MI) electroencephalogram (EEG) signal... more Many studies have reported the usefulness of motor imagery (MI) electroencephalogram (EEG) signals for Brain Computer Interface (BCI) systems. MI has been broadly characterized by the average of event-related changes of brain activity at specific frequency bands; but, temporal features of EEG have rarely been considered to identify different mental states of BCIs’ users. Additionally, complex classification techniques may have been proposed to enhance the accuracy of system but they may cause a notable delay during online applications. This paper investigated the application of neural network-based algorithms to classify three-class MIs by utilizing EEG time-domain features. Integrated EEG (IEEG) and Root Mean Square (RMS) features were extracted from EEG signals. Then, Multilayer Perceptron and Radial Basis Function Neural Networks were employed to classify the features. The discrimination ratio of such features were examined and compared through different classifiers. Moreover, the robustness of classifiers was investigated and compared. The results of this study indicated that RMS was more capable than IEEG for characterizing MI movements and RBF was more accurate and faster than MLP. The effectiveness of IEEG and RMS features and the performance of MLP and RBF classifiers were compared with Willison Amplitude (WAMP) feature and support vector machine (SVM) classifier respectively. This study proved that WAMP and SVM were more efficient for classification of MI tasks in both terms of accuracy (88.96%) and training time (0.5 second); however, considerable difference was not observed since RBF performed as fast as SVM with only about 3% less accuracy.
Background
This study proposes cross-match technique to detect the
presence of heart murmur in ph... more Background This study proposes cross-match technique to detect the presence of heart murmur in phonocardiogram. The sample data were recorded using an electronic stethoscope from real patient who suffers from structural heart disease such as Mitral Stenosis/Regurgitation and Aortic Stenosis/Regurgitation. The disease is confirmed by cardiologist with the support from an echocardiogram machine. Method The data were segmented into cardiac cycles manually. Each cycle is carefully observed to ensure that only clean data is accepted for the experiment. The features were extracted using Instantaneous Energy and Frequency (IEFE) method. The selected features reflect the sound originated from the structural defects or physical malfunction of the heart mechanics during the blood pumping activities. Cross- Match technique was then applied to train the input features to build the model barcode. Result The performance using the proposed method provides an accuracy of 86.4% which is comparable to the performance of other classifier such as support vector machine and neural network using the same experimental datasets. Conclusion It is concluded that cross-match performance is comparable with other classifier such as support vector machine and neural network. As it is very low in complexity and fast in computational time, is has a great potential to be used as heart murmur detection tool in medical facility such as primary care center.
This paper compared the application of multilayer perceptron (MLP) and radial basis function (RBF... more This paper compared the application of multilayer perceptron (MLP) and radial basis function (RBF) neural networks on a facial gesture recognition system. Electromyogram (EMG) signals generated by ten different facial gestures were recorded through three pairs of electrodes. EMGs were filtered and segmented into non-overlapped portions. The time-domain feature mean absolute value (MAV) and its two modified derivatives MMAV1 and MMAV2 were extracted. MLP and RBF were used to classify the EMG features while six types of activation functions were evaluated for MLP architecture. The discriminating power of single/multi features was also investigated. The results of this study showed that symmetric saturating linear was the most effective activation function for MLP; the feature set MAV + MMAV1 provided the highest accuracy by both classifiers; MLP reached higher recognition ratio for most of features; RBF was the faster algorithm which also offered a reliable trade-off between the two key metrics, accuracy and time.
Facial gesture recognition (FGR) is considered as a state-of-the-art which has drawn the research... more Facial gesture recognition (FGR) is considered as a state-of-the-art which has drawn the researchers’ attention in numerous fields of study due to its high potential in different applications. Recognizing the gestures through bio-signals generated from facial muscle movements has been recently proposed as an accurate and reliable pathway. The performance of gesture recognition-based systems directly depends on the effectiveness of classification techniques. Besides, a reasonable trade-off between recognition accuracy and computational cost is counted as the most significant factor for designing such systems. The aim of this paper was the classification of facial gestures electromyogram (EMG) signals by means of a least square support vector machine (LS-SVM) algorithm. Ten predefined facial gestures EMGs were recorded from ten participants through three bi-polar channels. Acquired signals were preprocessed using a band-pass filter and a segmentation technique. Then, time-domain features mean absolute value (MAV) and root mean square (RMS) were extracted from each segment. In order to classify the features, LS-SVM was implemented by considering radial basis function kernel and two multiclass encoding schemes, one-versus-one (OVO) and oneversus- all (OVA). This research showed that LS-SVM was a robust method for classification of facial gestures with 97.1% classification accuracy and 1.37 seconds training time when utilizing the feature combination MAV+RMS and the encoding technique OVA. It was also concluded that LS-SVM outperformed SVM and fuzzy c-means classifiers in this field of study. The results of this paper can be used as efficient processing tools in designing reliable interfaces for FGR systems.
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Papers by Mahyar Hamedi
scalp neural recordings offer deep understanding of the brain
sensory and cognitive functions. This paper develops a state
space-based TV multivariate autoregressive (MVAR) model for
estimating TV-information flow (IF) recruited by different motor
imagery (MI) movements. TV model coefficients are computed
through Kalman filter (KF) by incorporating Kalman smoothing
approach and expectation-maximization algorithm for model
parameter estimation, KS-EM. Volume conduction (VC) problem
is also addressed by considering full noise covariate in
observation equation. An automated model initialization is also
implemented to deliver optimal estimates. TV-partial directed
coherence derived from the proposed model is applied for IF
analysis. The performance of KS-EM is assessed and compared
with dual extended KF and overlapping sliding window-based
MVAR models using simulated data. Finally, TV-IF during four
different MI movements is studied. Results show the superiority
of KS-EM for tracking the rapid signal parameter changes and
eliminating the VC effect in the sensor space EEG. Differences in
contralateral/ipsilateral TV-IF around alpha and lower beta
bands during each MI task reveal the high potential of this
feature for BCI applications.
from scalp EEG. Recent studies use time-varying multivariate
autoregressive (TV-MAR) models to better describe the changing connectivity between cortical regions where the TV coefficients are estimated by Kalman filter (KF) within a state-space framework. We extend this approach by incorporating Kalman smoothing (KS) to improve the KF estimates, and the expectation-maximization (EM) algorithm to infer the unknown model parameters from EEG. We also consider solving the volume conduction problem by modeling the induced instantaneous correlations using a full noise
covariate. Simulation results show the superiority of KS in tracking the coefficient changes. We apply two derived frequency domain measures i.e. TV partial directed coherence (TV-PDC) and TV directed transfer function (TV-DTF), to investigate dynamic causal interactions between motor areas in discriminating motor imagery (MI) of left and right hand. Event-related changes of information flows around beta-band, in a unidirectional way between left and right hemispheres are observed during MI. A difference in interhemispheric
connectivity patterns is found between left and righthand
movements, implying potential usage for BCI.
imagery (MI) electroencephalogram (EEG) signals for Brain
Computer Interface (BCI) systems. MI has been broadly
characterized by the average of event-related changes of brain
activity at specific frequency bands; but, temporal features of
EEG have rarely been considered to identify different mental
states of BCIs’ users. Additionally, complex classification
techniques may have been proposed to enhance the accuracy of system but they may cause a notable delay during online
applications. This paper investigated the application of neural
network-based algorithms to classify three-class MIs by utilizing
EEG time-domain features. Integrated EEG (IEEG) and Root
Mean Square (RMS) features were extracted from EEG signals.
Then, Multilayer Perceptron and Radial Basis Function Neural
Networks were employed to classify the features. The
discrimination ratio of such features were examined and
compared through different classifiers. Moreover, the robustness
of classifiers was investigated and compared. The results of this
study indicated that RMS was more capable than IEEG for
characterizing MI movements and RBF was more accurate and
faster than MLP. The effectiveness of IEEG and RMS features
and the performance of MLP and RBF classifiers were compared with Willison Amplitude (WAMP) feature and support vector machine (SVM) classifier respectively. This study proved that WAMP and SVM were more efficient for classification of MI
tasks in both terms of accuracy (88.96%) and training time (0.5
second); however, considerable difference was not observed since RBF performed as fast as SVM with only about 3% less
accuracy.
This study proposes cross-match technique to detect the
presence of heart murmur in phonocardiogram. The sample
data were recorded using an electronic stethoscope from real
patient who suffers from structural heart disease such as
Mitral Stenosis/Regurgitation and Aortic
Stenosis/Regurgitation. The disease is confirmed by
cardiologist with the support from an echocardiogram
machine.
Method
The data were segmented into cardiac cycles manually.
Each cycle is carefully observed to ensure that only clean
data is accepted for the experiment. The features were
extracted using Instantaneous Energy and Frequency (IEFE)
method. The selected features reflect the sound originated
from the structural defects or physical malfunction of the
heart mechanics during the blood pumping activities. Cross-
Match technique was then applied to train the input features
to build the model barcode.
Result
The performance using the proposed method provides an
accuracy of 86.4% which is comparable to the performance
of other classifier such as support vector machine and neural
network using the same experimental datasets.
Conclusion
It is concluded that cross-match performance is comparable
with other classifier such as support vector machine and
neural network. As it is very low in complexity and fast in
computational time, is has a great potential to be used as
heart murmur detection tool in medical facility such as
primary care center.
scalp neural recordings offer deep understanding of the brain
sensory and cognitive functions. This paper develops a state
space-based TV multivariate autoregressive (MVAR) model for
estimating TV-information flow (IF) recruited by different motor
imagery (MI) movements. TV model coefficients are computed
through Kalman filter (KF) by incorporating Kalman smoothing
approach and expectation-maximization algorithm for model
parameter estimation, KS-EM. Volume conduction (VC) problem
is also addressed by considering full noise covariate in
observation equation. An automated model initialization is also
implemented to deliver optimal estimates. TV-partial directed
coherence derived from the proposed model is applied for IF
analysis. The performance of KS-EM is assessed and compared
with dual extended KF and overlapping sliding window-based
MVAR models using simulated data. Finally, TV-IF during four
different MI movements is studied. Results show the superiority
of KS-EM for tracking the rapid signal parameter changes and
eliminating the VC effect in the sensor space EEG. Differences in
contralateral/ipsilateral TV-IF around alpha and lower beta
bands during each MI task reveal the high potential of this
feature for BCI applications.
from scalp EEG. Recent studies use time-varying multivariate
autoregressive (TV-MAR) models to better describe the changing connectivity between cortical regions where the TV coefficients are estimated by Kalman filter (KF) within a state-space framework. We extend this approach by incorporating Kalman smoothing (KS) to improve the KF estimates, and the expectation-maximization (EM) algorithm to infer the unknown model parameters from EEG. We also consider solving the volume conduction problem by modeling the induced instantaneous correlations using a full noise
covariate. Simulation results show the superiority of KS in tracking the coefficient changes. We apply two derived frequency domain measures i.e. TV partial directed coherence (TV-PDC) and TV directed transfer function (TV-DTF), to investigate dynamic causal interactions between motor areas in discriminating motor imagery (MI) of left and right hand. Event-related changes of information flows around beta-band, in a unidirectional way between left and right hemispheres are observed during MI. A difference in interhemispheric
connectivity patterns is found between left and righthand
movements, implying potential usage for BCI.
imagery (MI) electroencephalogram (EEG) signals for Brain
Computer Interface (BCI) systems. MI has been broadly
characterized by the average of event-related changes of brain
activity at specific frequency bands; but, temporal features of
EEG have rarely been considered to identify different mental
states of BCIs’ users. Additionally, complex classification
techniques may have been proposed to enhance the accuracy of system but they may cause a notable delay during online
applications. This paper investigated the application of neural
network-based algorithms to classify three-class MIs by utilizing
EEG time-domain features. Integrated EEG (IEEG) and Root
Mean Square (RMS) features were extracted from EEG signals.
Then, Multilayer Perceptron and Radial Basis Function Neural
Networks were employed to classify the features. The
discrimination ratio of such features were examined and
compared through different classifiers. Moreover, the robustness
of classifiers was investigated and compared. The results of this
study indicated that RMS was more capable than IEEG for
characterizing MI movements and RBF was more accurate and
faster than MLP. The effectiveness of IEEG and RMS features
and the performance of MLP and RBF classifiers were compared with Willison Amplitude (WAMP) feature and support vector machine (SVM) classifier respectively. This study proved that WAMP and SVM were more efficient for classification of MI
tasks in both terms of accuracy (88.96%) and training time (0.5
second); however, considerable difference was not observed since RBF performed as fast as SVM with only about 3% less
accuracy.
This study proposes cross-match technique to detect the
presence of heart murmur in phonocardiogram. The sample
data were recorded using an electronic stethoscope from real
patient who suffers from structural heart disease such as
Mitral Stenosis/Regurgitation and Aortic
Stenosis/Regurgitation. The disease is confirmed by
cardiologist with the support from an echocardiogram
machine.
Method
The data were segmented into cardiac cycles manually.
Each cycle is carefully observed to ensure that only clean
data is accepted for the experiment. The features were
extracted using Instantaneous Energy and Frequency (IEFE)
method. The selected features reflect the sound originated
from the structural defects or physical malfunction of the
heart mechanics during the blood pumping activities. Cross-
Match technique was then applied to train the input features
to build the model barcode.
Result
The performance using the proposed method provides an
accuracy of 86.4% which is comparable to the performance
of other classifier such as support vector machine and neural
network using the same experimental datasets.
Conclusion
It is concluded that cross-match performance is comparable
with other classifier such as support vector machine and
neural network. As it is very low in complexity and fast in
computational time, is has a great potential to be used as
heart murmur detection tool in medical facility such as
primary care center.