[Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network].

Dec 27, 2022Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi

Classifying Brain Signals for Multiple Imagined Movements Using Adaptive Time-Frequency Patterns and Neural Networks

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗

Abstract

The proposed algorithm achieved an average accuracy of 93.96% in four-task classification using EEG signals.

A multi-task EEG signal classification method was developed combining adaptive time-frequency common spatial pattern (CSP) and convolutional neural network (CNN).
Personalized rhythm characteristics of subjects were extracted through adaptive spectrum awareness.
Spatial characteristics were computed using a one-versus-rest approach for CSP.
Composite time-domain characteristics were characterized to create spatial-temporal frequency multi-level fusion features.
The method demonstrated improved classification accuracy compared to existing algorithms, with reduced accuracy range error between subjects.

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The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.

[Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network].

Abstract

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