A brain computer interface (BCI) system helps people with motor dysfunction interact with the external environment. With the advancement of technology, BCI systems have been applied in practice, but their practicability and usability are still greatly challenged. A large amount of calibration time is often required before BCI systems are used, which can consume the patient’s energy and easily lead to anxiety. This paper proposes a novel motion-assisted method based on a novel dual-branch multiscale auto encoder network (MSAENet) to decode human brain motion imagery intentions, while introducing a central loss function to compensate for the shortcomings of traditional classifiers that only consider inter-class differences and ignore intra-class coupling. The effectiveness of the method is validated on three datasets, namely BCIIV2a, SMR-BCI and OpenBMI, to achieve zero calibration of the MI-BCI system. The results show that our proposed network displays good results on all three datasets. In the case of subject-independence, the MSAENet outperformed the other four comparison methods on the BCIIV2a and SMR-BCI datasets, while achieving F1_score values as high as 69.34% on the OpenBMI dataset. Our method maintains better classification accuracy with a small number of parameters and short prediction times, and the method achieves zero calibration of the MI-BCI system.
Background: Brain-computer interface (BCI) system helps people with motor dysfunction interact with external environment. With the advancement of technology, the BCI system has been applied in practice, but its practicability and usability are still greatly challenged. A large amount of calibration time is often required before BCI systems are used, which can consume the patient’s energy and patience and can easily lead to anxiety. We propose a subject-independentzero calibration approach to solve this problem. Methods: A dual-branch multiscale autoencoder network(MSAENet) is proposed realize the subject-independent classification in motor imagery, aiming to realize the plug-and-play of BCI. Firstly, the network consists of a multiscale branch and an autoencoder (AE) for feature learning from different perspectives. Secondly, the covariance between the EEG signal and the common spatial pattern in the 8-30 Hz band was used as spatio-spectral features, and the feature pre-extracted information is used as the input of MSAENet. Finally, The network introduces a central loss function to improve the classification ability. Testing network generalizability on three publicly available datasets BCIIV2a, SMR-BCI, and OpenBMI. Results: The results show that our proposed network shows good results on all three datasets. In the case of subject-independence, MSAENet outperformed the other four comparison methods on the BCIIV2a and SMR-BCI datasets,while achieving F1 score values as high as 69. 34% on the OpenBMI dataset.Subject-dependent results with the best classification performance were significantly better than the other four advanced comparison methods. Our method can maintain a better classification accuracy while ensuring a small amount of parameters and a short prediction time. Conclusions: MSAENet verifies the following three points: (1)Spatial frequency domain features can extract effective information from raw EEG signals. (2)Two-branch multiscale feature fusion can extract features more comprehensively. (3)The integration of the central loss function compensates for the fact that Softmax classifiers only consider class spacing and ignore intra-class distance. Our proposed method achieves zero calibration, and the problem that requires a lot of calibration time in BCI applications is effectively solved.Keywords: BCI; Motor imagery; Subject-independent; Feature fusion; Zero Calibration
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