Classification of motor imagery BCI using multiband tangent space mapping

Islam, Rabiul; Tanaka, Toshihisa; Akter, Most. Sheuli; Molla, Khademul Islam

doi:10.1109/icdsp.2017.8096075

Cited by 7 publications

(10 citation statements)

References 22 publications

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“…By considering the problems of noise effects as well as subject-specific frequency bands, multiband approach with tangent space mapping (MTSM) was proposed in [31] to further improve MI-BCI's classification performance. The idea was to extract effective noise-robust features with respect to narrow band signals in a multiband tangent space framework.…”

Section: Introductionmentioning

confidence: 99%

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery

2018

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Section: Introductionmentioning

confidence: 99%

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery

2018

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“…In fact, the improvement brought by Riemannian geometry is due to the consideration of the non-linear information contained in the covariance matrices, thus better extracting features, which are usually discarded by the linear space filtering methods. On the basis, the multi-band Riemannian method can use a small amount of calibration data to extract the noise robust features, and achieve better results ( Islam et al, 2017 , 2018 ; Hersche et al, 2018 ). In order to further improve the multi-band Riemannian method, this article uses a non-parametric method of MANOVA based on the sum of squared distances ( Anderson, 2001 ) to select frequency bands that are separable for specific subjects, and multi-scale division is performed on the multi-channel EEG signals in these frequency bands.…”

Section: Discussionmentioning

confidence: 99%

“…More recently, Satyam et al, combined the two adaptive strategies of RETRAIN and REBIAS ( Shenoy et al, 2006 ) with MRDM and Fisher Geodesic Minimum Distance to Riemannian Mean (FgMDRM), and the result achieved an average classification accuracy of approximately 74% on the test set (Session 2) of the 2a data set of BCI Competition IV ( Kumar et al, 2019 ). Islam et al (2017) proposed a multi-band TSM method, which takes into account multiple frequency bands and helps to extract effective noise robust features for narrow-band signals, but the study did not consider the question of the subject-specific frequency band. However, MI-BCI is an active BCI.…”

Section: Introductionmentioning

confidence: 99%

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.

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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.

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“…In fact, the improvement brought by Riemannian geometry is due to the consideration of the non-linear information contained in the covariance matrices, thus better extracting features, which are usually discarded by the linear space filtering methods. On the basis, the multi-band Riemannian method can use a small amount of calibration data to extract the noise robust features, and achieve better results (Islam et al, 2017(Islam et al, , 2018Hersche et al, 2018). In order to further improve the multi-band Riemannian method, this article uses a non-parametric method of MANOVA based on the sum of squared distances (Anderson, 2001) to select frequency bands that are separable for specific subjects, and multi-scale division is performed on the multi-channel EEG signals in these frequency bands.…”

Section: Discussionmentioning

confidence: 99%

“…More recently, Satyam et al, combined the two adaptive strategies of RETRAIN and REBIAS (Shenoy et al, 2006) with MRDM and Fisher Geodesic Minimum Distance to Riemannian Mean (FgMDRM), and the result achieved an average classification accuracy of approximately 74% on the test set (Session 2) of the 2a data set of BCI Competition IV . Islam et al (2017) proposed a multiband TSM method, which takes into account multiple frequency bands and helps to extract effective noise robust features for narrow-band signals, but the study did not consider the question of the subject-specific frequency band. However, MI-BCI is an active BCI.…”

Section: Introductionmentioning

confidence: 99%

Brain-Computer Interfaces for Perception, Learning, and Motor Control

2021

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Classification of motor imagery BCI using multiband tangent space mapping

Cited by 7 publications

References 22 publications

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery

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

Brain-Computer Interfaces for Perception, Learning, and Motor Control

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