Feature extraction and classification for EMG signals using linear discriminant analysis

Uncorrelated trace ratio linear discriminant analysis for undersampled problems

Atrophy is one of the most common consequences of muscle disorder. This could be a result of both myopathy and neuropathy. Muscle atrophy becomes more possible as people age. As a result of this disorder, the amount and size of muscle fibers decrease, therefore a person cannot produce high amount of force in his/her muscles, leading to difficulties in handling daily activities. The main purpose of this research is to find a way to predict this disorder. In this study the force classification was used for the atrophy disorder detection. The results show that different classifiers and features from the proposed ones, work for this purpose. To approach this goal, data were collected by recording surface EMG (sEMG) signals. Processing the recorded signals, best features with respect to more accuracy and less calculation complexity were selected and reported. After extracting the features from each patient with using different types of classifiers including LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis) and SVM (Support Vector Machine), the best approach to separate normal and atrophic people was investigated. It is found that unlike the proposed features such as MAV (Mean Absolute Value), SSC (Slope Sign Change) and WL (Waveform Length) in upper limb movement classification, three features WL, WAMP (Wilson Amplitude) (time domain features) and MNP (Mean Power) (frequency domain feature) show better performance for atrophy characterization. The results show that these features well predict the detection of biceps atrophy.

From disease detection to action assessment EMG signals are used variety of field. Miscellaneous studies have been conducted toward analysis of EMG signals. In this study some statistical features of signal were derived, the best evocative features were selected via Linear Discriminant Analysis (LDA) and feature vectors were constructed. This analytic feature vectors were classified through Extreme Learning Machine (ELM). 8 channel EMG signals recorded from 10 normal and 10 aggressive actions were used as an example. By cross-comparison of the obtained results to the ones obtained via various feature identifying methods (AR coefficients, wavelet energy and entropy) and classification methods (NB, SVM, LR, ANN, PART, Jrip, J48 and LMT) the success of the proposed method was determined.

The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds); that is, the data dimension is large compared to the number of data points (such data is said to be undersampled). To cope with performance and accuracy problems associated with high dimensionality, it is commonplace to apply a preprocessing step that transforms the data to a space of significantly lower dimension with limited loss of the information present in the original data. Linear Discriminant Analysis (LDA) is a well-known technique for dimension reduction and feature extraction, but it is not applicable for undersampled data due to singularity problems associated with the matrices in the underlying representation. This paper presents a dimension reduction and feature extraction scheme, called Uncorrelated Linear Discriminant Analysis (ULDA), for undersampled problems and illustrates its utility on gene expression data. ULDA employs the Generalized Singular Value Decomposition method to handle undersampled data and the features that it produces in the transformed space are uncorrelated, which makes it attractive for gene expression data. The properties of ULDA are established rigorously and extensive experimental results on gene expression data are presented to illustrate its effectiveness in classifying tissue samples. These results provide a comparative study of various state-of-the-art classification methods on well-known gene expression data sets.

Recent work on myoelectric prosthetic control has shown that the incorporation of accelerometry information along with surface electromyography (sEMG) has the potential of improving the performance and robustness of a prosthetic device by increasing the classification accuracy. In this study, we investigated whether myoelectric control could further benefit from the use of additional sensory modalities such as gyroscopes and magnetometers. We trained a multi-class linear discriminant analysis (LDA) classifier to discriminate between six hand grip patterns and used predictions to control a robotic prosthetic hand in real-time. We recorded initial training data by using a total number of 12 sEMG sensors, each of which integrated a 9 degree-of-freedom inertial measurement unit (IMU). For classification, four different decoding schemes were used; 1) sEMG and IMU from all sensors 2) sEMG from all sensors, 3) IMU from all sensors and, finally, 4) sEMG and IMU from a nearly optimal subset of sensors. These schemes were evaluated based on offline classification accuracy on the training data, as well as with task-related metrics such as completion rates and times for a pick-and-place real-time experiment. We found that the classifier trained with all the sensory modalities and sensors (condition 1) attained the best decoding performance by achieving a 90.4% completion rate with an average completion time of 41.9 sec in real-time experiments. We also found that classifiers incorporating sEMG and IMU information outperformed on average the ones that only used sEMG signals, even when the amount of sensors used was less than half in the former case. These results suggest that using extra modalities along with sEMG might be more beneficial than including additional sEMG sensors.

The abundant movement information carried by the surface electromyography (sEMG) signal can comprehensively reflect the muscle movement patterns on the surface of the human body, and the characteristics of physiological signals involving movement information have promoted the vigorous development in the field of human-computer interaction in machine learning. This paper takes subtle gestures as the research objects, and proposes a subtle gestures recognition system that uses two-channel sEMG signal and utilizes Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) which are parameter estimation classifiers based on probability density. First, combining the raw sEMG signal and the envelope sEMG signal, the number of channels for sensors and corresponding position of the muscle groups are preferably selected. Then, we propose to use the Pearson correlation coefficient to optimize the four types of features extracted in the time domain, frequency domain, time-frequency domain and AR model parameters, which effectively ensures the uniqueness of each gesture and eliminates individual differences. Finally, this paper compares four classifiers of QDA, LDA, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), and explores the pattern recognition classifier suitable for subtle actions. The experiment collects 9 kinds of subtle gestures from 8 subjects. The results show that the system can use only two channels of sEMG signal to recognize 9 kinds of subtle gestures which include multi-finger clicking or pinching or holding objects, and the most suitable classifiers QDA and LDA have the average recognition rates of 95.79% and 95.01%, respectively.

The objective of this work is to study the EMG signals based on hand motions for specified tasks, and different gripping conditions so as to identify patterns of EMG signals. This will allow therapists to identify weak muscles of patients with motor weakness, such as spinal cord injury (SCI) and post-stroke and concentrate on rehabilitation activities which can strengthen these specific muscles. At the same time, it is hoped that the analysis is able to provide useful data for objective and quantitative assessment towards control applications on the hand rehabilitation device, which is being developed. The analysis of the EMG signals for various hand muscles during functional motions and prehensile tasks has been carried out. The method to identify individual hand motions from EMG signals is described. The EMG signals associated to physical parameters are also illustrated in this paper. Finally, the preliminary works and future research are concluded.

Sleep Disordered Breathing describes a group of disorders characterized by abnormalities of respiratory pattern or the quantity of ventilation during sleep. Sleep Disordered Breathing causes disruptions in sleep, yielding waking somnolence, diminished neurocognitive performance, adverse cardiovascular outcomes, insulin resistance and other metabolic dysfunctions. Electroencephalography and Electromyography signal analysis enables neurologists and sleep specialists to diagnose and monitor Sleep Disordered Breathing events. This paper look into the prospect of identifying sleep disordered breathing events through the analysis of Electroencephalograph and Electromyography signals. EEG and EMG signals corresponding to 25 subjects with previously diagnosed with Sleep Disorder Breathing studied and analyzed. The detection of Sleep Disordered Breathing events were based on the study of power spectral analysis of EEG and EMG signals. The variations in the normalized power of various spectral bands of EEG signals are studied along with the variations in power of the corresponding EMG signals. This paper also discusses the probable causes of the aforementioned variations in the EEG and EMG signals.

This work proposes a methodology for content-based image retrieval of glioblastoma multiforme (GBM) and non-GBM tumors. Regions containing GBM lesions from 40 patients and non-GBM lesions from 20 patients were manually segmented from MR imaging studies (T1 post-contrast and T2 weighted channels) to form the training set. In addition to the two acquired channels, a composite image was formed by an image fusion method. Data reduction techniques, principal component analysis (PCA) and linear discriminant analysis (LDA), were applied on the training sets (T1 post, T2, composite, and multi-channel combining the PCA features from T1 post and T2). The retrieval accuracy was evaluated using a 'leave-one-out' strategy with query images belonging to 'normal', 'GBM' and 'non-GBM' classes. Several combinations of the similarity metric and classifier were used: Euclidean similarity measures with k-means classifier for the PCA and LDA features and support vector machine (SVM) nonlinear classifier (radial basis function kernel) with the PCA derived features. The SVM classifier served as a comparison of nonlinear techniques vs. linear ones. Multi-channel PCA was 100% accurate in classifying a query image as either 'normal' or 'abnormal'. The highest accuracy in classification of tumor grade (GBM or other Grade 3) was 77% and was achieved by SVM coupled with the PCA features. The proposed algorithm intent is to be integrated into an automated decision support system for MR brain tumor studies.

This paper devoted to the optimization of surface electromyography (sEMG) based hand gesture recognition classifier model. Twenty-two features in time domain, frequency domain and time-frequency domain were extracted from sEMG of 6 muscles to classify 13 hand gestures. Correlation analysis was used to screen the features. Random forest (RF) and principal component analysis (PCA) feature selection were used to further reduce the feature dimensions. Finally, mean absolute value (MAV), root mean square (RMS), slope sign change (SSC), mean frequency (MNF), Willison amplitude (WAMP) and autoregressive coefficients (AR1), totally 6 features were selected. LDA (Linear Discriminant Analysis), SVM (Support Vector Machine), KNN (k-Nearest Neighbor), DT (Decision Tree) and NN (Neural Network), in total of five classification methods were applied. The accuracy of the classifier had no significant reduction, but the dimension of the feature set was reduced from 132 to 36, and the training time was greatly reduced. The result indicated that the combination of correlation analysis and random forest feature selection can optimize the classifier model and greatly reduce the training time of the classifier.

This paper presents a novel and interesting combination of uncorrelated linear discriminant analysis and wavelets to extract features for face recognition. The proposed algorithm when compared with conventional Fisherface method and ULDA+PCA method has an improved recognition rate and a decrease of computational load for face images with high resolutions. In the proposed technique, the face images are divided into smaller sub-images by 2-D DWT and the uncorrelated linear discriminant analysis is applied to approximations sub-images. The time-cost of the proposed method is greatly reduced and recognition rates ranging between 95% and 97.5% are obtained on the ORL database. An average error rate of 1.4% is obtained with the experiments on the NUST603 database. In addition, the effect of number of discriminant vectors on the recognition system is systematically discussed.

A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study

BackgroundProcessing the surface electromyogram (sEMG) to decode movement intent is a promising approach for natural control of upper extremity prostheses. To this end, this paper introduces and evaluates a new framework which allows for simultaneous and proportional myoelectric control over multiple degrees of freedom (DoFs) in real-time. The framework uses multitask neural networks and domain-informed regularization in order to automatically find nonlinear mappings from the forearm sEMG envelope to multivariate and continuous encodings of concurrent hand- and wrist kinematics, despite only requiring categorical movement instruction stimuli signals for calibration.MethodsForearm sEMG with 8 channels was collected from healthy human subjects (N = 20) and used to calibrate two myoelectric control interfaces, each with two output DoFs. The interfaces were built from (I) the proposed framework, termed Myoelectric Representation Learning (MRL), and, to allow for comparisons, from (II) a standard pattern recognition framework based on Linear Discriminant Analysis (LDA). The online performances of both interfaces were assessed with a Fitts’s law type test generating 5 quantitative performance metrics. The temporal stabilities of the interfaces were evaluated by conducting identical tests without recalibration 7 days after the initial experiment session.ResultsMetric-wise two-way repeated measures ANOVA with factors method (MRL vs LDA) and session (day 1 vs day 7) revealed a significant (p<0.05) advantage for MRL over LDA in 5 out of 5 performance metrics, with metric-wise effect sizes (Cohen’s d) separating MRL from LDA ranging from left|dright|=0.62 to left|dright|=1.13. No significant effect on any metric was detected for neither session nor interaction between method and session, indicating that none of the methods deteriorated significantly in control efficacy during one week of intermission.ConclusionsThe results suggest that MRL is able to successfully generate stable mappings from EMG to kinematics, thereby enabling myoelectric control with real-time performance superior to that of the current commercial standard for pattern recognition (as represented by LDA). It is thus postulated that the presented MRL approach can be of practical utility for muscle-computer interfaces.

The upper-limb rehabilitation robots can be developed as an efficient tool for motor function assessments. Circle-drawing has been used as a specific task for robot-based motor function measurement. The upper-limb movement-related kinematic and kinetic parameters measured by motion and force sensors embedded in the rehabilitation robots have been widely studied. However, the muscle synergies characterized by multiple surface electromyographic (sEMG) signals in upper limbs during human-robot interaction (HRI) with circle-drawing movements are rarely investigated. In this research, the robot-assisted and constrained circle-drawing movements for upper limb were used to increase the consistency of muscle synergy features. Both clockwise and counterclockwise circle-drawing tasks were implemented by all healthy subjects using right hands. The sEMG signals were recorded from six muscles in upper limb, and nonnegative matrix factorization (NMF) analysis was utilized to obtain muscle synergy information. Both synergy pattern and activation coefficient were calculated to represent the spatial and temporal features of muscle synergies, respectively. The results obtained from the experimental study confirmed that high structural similarity of muscle synergies was found among the subjects during HRI with circle-drawing movement by healthy subjects, which indicates healthy people may share a common underlying muscle control mechanism during constrained upper-limb circle-drawing movement. This study indicates the muscle synergy analysis during the HRI with constrained circle-drawing movement could be considered as a task for upper-limb motor function assessment.

Various exoskeletons have been developed for the assistance of upper and lower limbs rehabilitation, however, therapists and doctors have pointed out that passive-repeating motions of limbs is ineffective in terms of neurological recovery. To improve the neurological recovery of human motor control, active-assisted encouragement as therapist and doctors manually provided should also be simulated with help of exoskeleton targeting for rehabilitative purpose. In this paper, a single-degree-of-freedom (SDOF) robotic joint to mimic elbow movements is developed and EMG signals are collected using as a bio-feedback signal. A pair of muscles including biceps and triceps that involved in flexion and extension on the elbow joint are selected, acquisition and processing of EMG signals is then achieved by designed hardware filtering and proposed software algorithms. By analysis of the resultant EMG signals, the intentions of flexion and extension movements are quantitatively presented. Online control commands for the exoskeleton are then spontaneously produced. A close-loop control with EMG-driven in real time is applied on an initial prototype of human-machine interface (HMI) for an active assistance using the exoskeleton. Initial applications on the experimental prototype have proved the effectiveness of the proposed active tracking method that has successfully tracking effort that bridge the wearer and the wearable exoskeleton together.