Abstract
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.
Highlights
Brain-computer interface (BCI) is a revolutionizing humancomputer interaction (Graimann et al, 2010), and BCI based on motor imagery (MI-BCI) is an important type of BCI which is expected to provide communication and control with the outside world for patients with severe motor disabilities (Wolpaw and Wolpaw, 2012), especially in motor dysfunction rehabilitation training (Soares et al, 2013)
In order to show the justifiability of using the F score of each subband as the criterion for selecting a separable frequency band, the classification accuracy of different sub-bands of the test data of different subjects on the BCI competition public data set is calculated, as shown in Figure 5, where the sub-band width for calculating the classification accuracy is 4 Hz, and the range is from 4 to 36 Hz
Fisher Geodesic Minimum Distance to Riemannian Mean (FgMDRM) classifier is first trained on training/calibration session data, during the testing session, the classifier is retrained after each prediction (Kumar et al, 2019)
Summary
Brain-computer interface (BCI) is a revolutionizing humancomputer interaction (Graimann et al, 2010), and BCI based on motor imagery (MI-BCI) is an important type of BCI which is expected to provide communication and control with the outside world for patients with severe motor disabilities (Wolpaw and Wolpaw, 2012), especially in motor dysfunction rehabilitation training (Soares et al, 2013). Neuroscience research has shown that brain activities related to MI and motor execution (ME) can cause similar sensorimotor rhythm changes (Pfurtscheller and Neuper, 1997), and the EEG amplitude of certain frequency bands will decrease eventrelated desynchronization (ERD) or increase event related synchronization (ERS). This ERD/ERS phenomenon or pattern is most prominent in mu rhythm (8–12 Hz) and beta rhythm (13–30 Hz), and can be observed in gamma rhythm close to 40 Hz (Rao, 2013).
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