Feature Extraction of Motor Imagery EEG Based on Extreme Learning Machine Auto-encoder

2016 ◽  
pp. 361-370 ◽  
Author(s):  
Lijuan Duan ◽  
Yanhui Xu ◽  
Song Cui ◽  
Juncheng Chen ◽  
Menghu Bao
Keyword(s):  
Feature Extraction ◽  
Extreme Learning Machine ◽  
Motor Imagery ◽  
Learning Machine

scholarly journals Embedding Tangent Space Extreme Learning Machine for EEG Decoding in Brain Computer Interface Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mingwei Zhang ◽  
Yao Hou ◽  
Rongnian Tang ◽  
Youjun Li
Keyword(s):  
Extreme Learning Machine ◽  
Motor Imagery ◽  
Tangent Space ◽  
Brain Computer Interface ◽  
Covariance Matrices ◽  
Computer Interface ◽  
Spatial Covariance ◽  
Learning Machine ◽  
Low Dimensional ◽  
Bci System

In motor imagery brain computer interface system, the spatial covariance matrices of EEG signals which carried important discriminative information have been well used to improve the decoding performance of motor imagery. However, the covariance matrices often suffer from the problem of high dimensionality, which leads to a high computational cost and overfitting. These problems directly limit the application ability and work efficiency of the BCI system. To improve these problems and enhance the performance of the BCI system, in this study, we propose a novel semisupervised locality-preserving graph embedding model to learn a low-dimensional embedding. This approach enables a low-dimensional embedding to capture more discriminant information for classification by efficiently incorporating information from testing and training data into a Riemannian graph. Furthermore, we obtain an efficient classification algorithm using an extreme learning machine (ELM) classifier developed on the tangent space of a learned embedding. Experimental results show that our proposed approach achieves higher classification performance than benchmark methods on various datasets, including the BCI Competition IIa dataset and in-house BCI datasets.

scholarly journals Remaining Useful Life Prediction of Rolling Bearings Using PSR, JADE, and Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Yongbin Liu ◽  
Bing He ◽  
Fang Liu ◽  
Siliang Lu ◽  
Yilei Zhao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Phase Space ◽  
Extreme Learning Machine ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Phase Point ◽  
Rolling Bearings ◽  
Point Distribution ◽  
Useful Life ◽  
Learning Machine

Rolling bearings play a pivotal role in rotating machinery. The degradation assessment and remaining useful life (RUL) prediction of bearings are critical to condition-based maintenance. However, sensitive feature extraction still remains a formidable challenge. In this paper, a novel feature extraction method is introduced to obtain the sensitive features through phase space reconstitution (PSR) and joint with approximate diagonalization of Eigen-matrices (JADE). Firstly, the original features are extracted from bearing vibration signals in time and frequency domain. Secondly, the PSR is applied to embed the original features into high dimensional phase space. The between-class and within-class scatter (SS) are calculated to evaluate the feature sensitivity through the phase point distribution of different degradation stages and then different weights are assigned to the corresponding features based on the calculatedSS. Thirdly, the JADE is employed to fuse the weighted features to obtain the advanced features which can better reflect the bearing degradation process. Finally, the advanced features are input into the extreme learning machine (ELM) to train the RUL prediction model. A set of experimental case studies are carried out to verify the effectiveness of the proposed method. The results show that the extracted advanced features can better reflect the degradation process compared to traditional features and could effectively predict the RUL of bearing.

IoT and wearables for detection of COVID-19 diagnosis using fusion-based feature extraction with multikernel extreme learning machine

2022 ◽  
pp. 137-152
Author(s):  
Ganesh Kumar R. ◽  
Srilatha Toomula ◽  
D. Paulraj ◽  
Jebin Bose S. ◽  
Thulasi Bikku ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extreme Learning Machine ◽  
Learning Machine

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

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Qingshan She ◽  
Kang Chen ◽  
Yuliang Ma ◽  
Thinh Nguyen ◽  
Yingchun Zhang
Keyword(s):  
Sparse Representation ◽  
Extreme Learning Machine ◽  
Motor Imagery ◽  
Vital Role ◽  
Superior Performance ◽  
Common Spatial Pattern ◽  
Significant Information ◽  
Feature Representations ◽  
Novel Approach ◽  
Learning Machine

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.

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