Qingshan She

Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals.

EMG signals based gait phases recognition using hidden Markov models

The application of hidden Markov model (HMM) to recognize gait phase using electromyographic (EMG) signals is described. Four time-domain features are extracted within a time segment of each channel of EMG signals to preserve pattern structure. According to the division of the gait cycle, the structure of HMM is determined, in which each state is associated with a gait phase. A modified Baum-Welch algorithm is used to estimate the parameter of HMM. And Viterbi algorithm achieves the phase recognition by finding the best state sequence to assign corresponding phases to the given segments. The feature set and data segmentation manner yielded high rate of accuracy are ascertained through evaluation experiments.

Scale-dependent signal identification in low-dimensional subspace: Motor imagery task classification

Motor imagery electroencephalography (EEG) has been successfully used in locomotor rehabilitation programs. While the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm has been utilized to extract task-specific frequency bands from all channels in the same scale as the intrinsic mode functions (IMFs), identifying and extracting the specific IMFs that contain significant information remain difficult. In this paper, a novel method has been developed to identify the information-bearing components in a low-dimensional subspace without prior knowledge. Our method trains a Gaussian mixture model (GMM) of the composite data, which is comprised of the IMFs from both the original signal and noise, by employing kernel spectral regression to reduce the dimension of the composite data. The informative IMFs are then discriminated using a GMM clustering algorithm, the common spatial pattern (CSP) approach is exploited to extract the task-related features from the reconstructed signals, and a support vector machine (SVM) is applied to the extracted features to recognize the classes of EEG signals during different motor imagery tasks. The effectiveness of the proposed method has been verified by both computer simulations and motor imagery EEG datasets.

Automatic recognition of gait mode from EMG signals of lower limb

This paper describes the application of hidden Markov model (HMM) to recognition of gait mode based on electromyographic (EMG) signals. Four types of time-domain features were extracted from the EMG signals of selected muscles within a time segment. According to the division of the gait phase, the structure of HMM was determined, in which each state is associated with a gait phase. A modified Baum-Welch algorithm was used to estimate the parameter of HMM. And Viterbi algorithm achieved the recognition of gait mode by finding the best HMM and state to assign corresponding phases to the given segments. The locomotion modes could be recognized nearly all correct in evaluation experiments. A satisfactory accuracy in the recognition of gait phase also was obtained.

Multiclass posterior probability twin SVM for motor imagery EEG classification

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platts estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

Multiple kernel learning SVM-based EMG pattern classification for lower limb control

Based on multiple kernel learning (MKL) support vector machine and decision tree combined strategy, a multi-class classification method is proposed to classify lower limb motions using electromyography (EMG) signals. According to the framework of multiple kernel learning, the MKL-based multi-classifier is constructed using binary tree decomposition method. Four-channel surface EMG signals are firstly collected from lower limb muscles, and then some time-domain features are extracted and inputted into the proposed multi-classifier. Five subdividing patterns are finally identified in level walking, i.e. support prophase, support metaphase, support telophase, swing prophase and swing telophase. The experimental results show that the proposed method can successfully identify these subdividing patterns with better accuracy than standard single-kernel support vector machine classifier.

A risk degree-based safe semi-supervised learning algorithm

AbstractSemi-supervised learning has attracted much attention in machine learning field over the past decades and a number of algorithms are proposed to improve the performance by exploiting unlabeled data. However, unlabeled data may hurt performance of semi-supervised learning in some cases. It is instinctively expected to design a reasonable strategy to safety exploit unlabeled data. To address the problem, we introduce a safe semi-supervised learning by analyzing the different characteristics of unlabeled data in supervised and semi-supervised learning. Our intuition is that unlabeled data may be often risky in semi-supervised setting and the risk degree are different. Hence, we assign different risk degree to unlabeled data and the risk degree serve as a sieve to determine the exploiting way of unlabeled data. The unlabeled data with high risk should be exploited by supervised learning and the other should be used for semi-supervised learning. In particular, we utilize kernel minimum squared error (KMSE) and Laplacian regularized KMSE for supervised and semi-supervised learning, respectively. Experimental results on several benchmark datasets illustrate the performance of our algorithm is never inferior to that of KMSE and indicate the effectiveness and efficiency of our algorithm.

A hierarchical semi-supervised extreme learning machine method for EEG recognition

AbstractFeature extraction and classification is a vital part in motor imagery-based brain-computer interface (BCI) system. Traditional deep learning (DL) methods usually perform better with more labeled training samples. Unfortunately, the labeled samples are usually scarce for electroencephalography (EEG) data, while unlabeled samples are available in large quantity and easy to collect. In addition, traditional DL algorithms are notoriously time-consuming for the training process. To address these issues, a novel method of hierarchical semi-supervised extreme learning machine (HSS-ELM) is proposed in this paper and applied for motor imagery (MI) task classification. Firstly, the deep architecture of hierarchical ELM (H-ELM) approach is employed for feature learning automatically, and then these new high-level features are classified using the semi-supervised ELM (SS-ELM) algorithm which can exploit the information from both labeled and unlabeled data. Extensive experiments were conducted on some benchmark datasets and EEG datasets to evaluate the effectiveness of the proposed method. Compared with several state-of-the-art methods, including SVM, ELM, SAE, H-ELM, and SS-ELM, our HSS-ELM method can achieve better classification accuracy, a mean kappa value of 0.7945 and 0.5701 across all subjects in the training and evaluation sessions of BCI Competition IV Dataset 2a, respectively. Finally, it comes to the conclusion that the proposed method has achieved superior performance for feature extraction and classification of EEG signals. Graphical abstractThe schematic of the proposed HSS-ELM algorithm.

Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification

Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant information which leads to a less efficient classification. In this paper, we propose a novel approach called FDDL-ELM, which combines the discriminative power of extreme learning machine (ELM) with the reconstruction capability of sparse representation. Firstly, the common spatial pattern (CSP) algorithm is adopted to perform spatial filtering on raw EEG data to enhance the task-related neural activity. Secondly, the Fisher discrimination criterion is employed to learn a structured dictionary and obtain sparse coding coefficients from the filtered data, and these discriminative coefficients are then used to acquire the reconstructed feature representations. Finally, a nonlinear classifier ELM is used to identify these features in different MI tasks. The proposed method is evaluated on 2-class Datasets IVa and IIIa of BCI Competition III and 4-class Dataset IIa of BCI Competition IV. Experimental results show that our method achieved superior performance than the other existing algorithms and yielded the accuracies of 80.68%, 87.54%, and 63.76% across all subjects in the above-mentioned three datasets, respectively.

Generalization improvement for regularized least squares classification

In the past decades, regularized least squares classification (RLSC) is a commonly used supervised classification method in the machine learning filed because it can be easily resolved through the simple matrix analysis and achieve a close-form solution. Recently, some studies conjecture that the margin distribution is more crucial to the generalization performance. Moreover, from the view of margin distribution, RLSC only considers the first-order statistics (i.e., margin mean) and does not consider the actual higher-order statistics of margin distribution. In this paper, we propose a novel RLSC which takes into account the actual second-order (i.e., variance) information of margin distribution. It is intuitively expected that small margin variance will improve the generalization performance of RLSC from a geometric view. We incorporate the margin variance into the objective function of RLSC and achieve the optimal classifier by minimizing the margin variance. To evaluate the performance of our algorithm, we conduct a series of experiments on several benchmark datasets in comparison with RLSC, kernel minimum squared error, support vector machine and large margin distribution machine. And the empirical results verify the effectiveness of our algorithm and indicate that the margin distribution is helpful to improve the classification performance.