Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

Xu, Jiacan; Zheng, Hao; Wang, Jianhui; Li, Donglin; Fang, Xiaoke

doi:10.3390/s20123496

Cited by 55 publications

(43 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, this article is compared with the latest three Deep Learning-based methods. In Deep Multi-view feature learning method ( Xu et al, 2020 ), the author uses the improved, the deep restricted Boltzmann machine (RBM) network to learn to learn the multi-view features of EEG signals, and finally uses SVM to classify deep multi-view features. The DFFN algorithm is a dense feature fusion convolutional neural network using CSP and ConvNet technology ( Li et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…Tables 1 , 2 present the mean and standard deviation of the classification accuracy (averaged across all the subjects) on a session to session transfer evaluation for these methods. The results presented in Table 3 are obtained by combining and randomly arranging the training data (Session 1) and test data (Session 2) of each subject’s data set according to the data organization method in Xu et al (2020) , and then performing 10 fold cross-validation.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, Zhang et al (2015) proposed the sparse filter bank CSP (SFBCSP) in which a small number of sub-band CSP features are automatically selected based on LASSO (least absolute shrinkage and selection operator) regression. According to some recent achievements, we know that a breakthrough has been made in the research of MI task recognition based on Deep Learning ( Li et al, 2019 ; Olivas Padilla and Chacon Murguia, 2019 ; Xu et al, 2020 ). In Xu et al (2020) , a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals is proposed in order to obtain more representative motor imagery features in EEG signals.…”

Section: Introductionmentioning

confidence: 99%

“…According to some recent achievements, we know that a breakthrough has been made in the research of MI task recognition based on Deep Learning ( Li et al, 2019 ; Olivas Padilla and Chacon Murguia, 2019 ; Xu et al, 2020 ). In Xu et al (2020) , a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals is proposed in order to obtain more representative motor imagery features in EEG signals. In Li et al (2019) , the researchers proposes a variant of Discriminative Filter Bank Common Spatial Pattern (DFBCSP) for extracting MI features, and then sets the resulting samples into a matrix, which is then fed to one or many ConvNets previously optimized by using a Bayesian optimization for classification.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Subject-Specific Discriminative and Multi-Scale Filter Bank Tangent Space Mapping Method for Recognition of Multiclass Motor Imagery

Gong

Liu

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Objective: Tangent Space Mapping (TSM) using the geometric structure of the covariance matrices is an effective method to recognize multiclass motor imagery (MI). Compared with the traditional CSP method, the Riemann geometric method based on TSM takes into account the nonlinear information contained in the covariance matrix, and can extract more abundant and effective features. Moreover, the method is an unsupervised operation, which can reduce the time of feature extraction. However, EEG features induced by MI mental activities of different subjects are not the same, so selection of subject-specific discriminative EEG frequency components play a vital role in the recognition of multiclass MI. In order to solve the problem, a discriminative and multi-scale filter bank tangent space mapping (DMFBTSM) algorithm is proposed in this article to design the subject-specific Filter Bank (FB) so as to effectively recognize multiclass MI tasks.Methods: On the 4-class BCI competition IV-2a dataset, first, a non-parametric method of multivariate analysis of variance (MANOVA) based on the sum of squared distances is used to select discriminative frequency bands for a subject; next, a multi-scale FB is generated according to the range of these frequency bands, and then decompose multi-channel EEG of the subject into multiple sub-bands combined with several time windows. Then TSM algorithm is used to estimate Riemannian tangent space features in each sub-band and finally a liner Support Vector Machines (SVM) is used for classification.Main Results: The analysis results show that the proposed discriminative FB enhances the multi-scale TSM algorithm, improves the classification accuracy and reduces the execution time during training and testing. On the 4-class BCI competition IV-2a dataset, the average session to session classification accuracy of nine subjects reached 77.33 ± 12.3%. When the training time and the test time are similar, the average classification accuracy is 2.56% higher than the latest TSM method based on multi-scale filter bank analysis technology. When the classification accuracy is similar, the training speed is increased by more than three times, and the test speed is increased two times more. Compared with Supervised Fisher Geodesic Minimum Distance to the Mean (Supervised FGMDRM), another new variant based on Riemann geometry classifier, the average accuracy is 3.36% higher, we also compared with the latest Deep Learning method, and the average accuracy of 10-fold cross validation improved by 2.58%.Conclusion: Research shows that the proposed DMFBTSM algorithm can improve the classification accuracy of MI tasks.Significance: Compared with the MFBTSM algorithm, the algorithm proposed in this article is expected to select frequency bands with good separability for specific subject to improve the classification accuracy of multiclass MI tasks and reduce the feature dimension to reduce training time and testing time.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Subject-Specific Discriminative and Multi-Scale Filter Bank Tangent Space Mapping Method for Recognition of Multiclass Motor Imagery

Gong

Liu

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Maximum classification accuracy of 85.6% was obtained for decision tree classifier. Jiacan et al [ 28 ] presents a deep multi-view feature learning process for the classification of motor imagery EEG tasks. First, many multidomain features (time, frequency, time-frequency, and spatial) were extracted, and then a restricted Boltzmann machine network improved by t-distributed stochastic neighbor embedding (t-SNE) is employed for features learning.…”

Section: Introductionmentioning

confidence: 99%

Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index

Sadiq

Yuan

et al. 2020

Sensors

View full text Add to dashboard Cite

The development of fast and robust brain–computer interface (BCI) systems requires non-complex and efficient computational tools. The modern procedures adopted for this purpose are complex which limits their use in practical applications. In this study, for the first time, and to the best of our knowledge, a successive decomposition index (SDI)-based feature extraction approach is utilized for the classification of motor and mental imagery electroencephalography (EEG) tasks. First of all, the public datasets IVa, IVb, and V from BCI competition III were denoised using multiscale principal analysis (MSPCA), and then a SDI feature was calculated corresponding to each trial of the data. Finally, six benchmark machine learning and neural network classifiers were used to evaluate the performance of the proposed method. All the experiments were performed for motor and mental imagery datasets in binary and multiclass applications using a 10-fold cross-validation method. Furthermore, computerized automatic detection of motor and mental imagery using SDI (CADMMI-SDI) is developed to describe the proposed approach practically. The experimental results suggest that the highest classification accuracy of 97.46% (Dataset IVa), 99.52% (Dataset IVb), and 99.33% (Dataset V) was obtained using feedforward neural network classifier. Moreover, a series of experiments, namely, statistical analysis, channels variation, classifier parameters variation, processed and unprocessed data, and computational complexity, were performed and it was concluded that SDI is robust for noise, and a non-complex and efficient biomarker for the development of fast and accurate motor and mental imagery BCI systems.

show abstract

Deep Learning‐Based Multiomics Data Integration Methods for Biomedical Application

Wen¹,

Zheng

Leng³

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

Driven by medical radiomics and high-throughput techniques, there has been explosive growth in the amount of advanced multiomics data in modern biomedical research. These data contain abundant information reflecting cellular context, allowing researchers to disentangle biomolecular mechanisms, and gain a more comprehensive understanding of biological processes related to complex diseases. [1,2] Single-omics data provide limited information on biological systems. The systematic study of biomedical objects by integrating and analyzing data at diverse scales is an emerging field. [3][4][5] The integration of multiomics data makes it possible to understand complex biological systems from different perspectives. [5,6] Using multiomics data can identify the best therapeutic targets and address key biomedical objects, including personalized complex disease therapy, [7][8][9][10][11] drug discovery, [12][13][14][15] and drug target discovery. [16][17][18][19][20] However, multiomics data are complex, high dimensional, and heterogeneous. [21,22] By far the most important challenge is how to extract valuable knowledge from these data. Since various omics data are obtained by different measurement techniques, the data distribution for different omics is different. The inconsistency of data distribution is one of the difficulties to be overcome. At the same time, each sample contains data from multiple omics, and exploring potential correlations between these data is also a problem. Biomedical data is also characterized by high dimensionality and few samples, which can generate the curse of dimensionality during data mining and reduce the generalization ability of the model. To address this challenge, researchers have developed methods such as multiple kernel learning, Bayesian approaches, dimensionality reduction approaches, network-based methods, and deep learning (DL) methods. [23][24][25][26][27][28][29][30] With the continuous development of DL, up to now, many DL methods have emerged to integrate multiomics data. The DL methods can be utilized as an efficient framework to process a large amount of multiomics, high-dimensional, and complex data. The DL-based methods can capture the typical nonlinearities and complex relationships in biological data to achieve more accurate predictions. DL, represented by multiomics data modeling, has achieved substantial success in biomedical fields. The combination of DL and multiomics data is significantly beneficial for the development of modern biomedical studies.In this article, we mainly focused on how to effectively extract omics data representations and how to integrate these representations. We classified multiomics integration models into six categories according to the framework of DL-based multiomics data integration methods. In addition, we reviewed and

show abstract

Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

Cited by 55 publications

References 44 publications

A New Subject-Specific Discriminative and Multi-Scale Filter Bank Tangent Space Mapping Method for Recognition of Multiclass Motor Imagery

A New Subject-Specific Discriminative and Multi-Scale Filter Bank Tangent Space Mapping Method for Recognition of Multiclass Motor Imagery

Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index

Deep Learning‐Based Multiomics Data Integration Methods for Biomedical Application

Contact Info

Product

Resources

About