Semi-supervised classifier with projection graph embedding for motor imagery electroencephalogram recognition

Ni, Tongguang; He, Chengbing; Gu, Xiaoqing

doi:10.1007/s11042-023-16010-8

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…Year Acc Kappa FBCSP [13] 2008 67.75% 0.5700 CCSP [14] 2009 66.51% 0.5535 ConvNet [16] 2017 72.53% 0.6337 EEGNet [17] 2018 74.61% 0.6615 Incep-EEGNet [18] 2020 74.07% 0.6543 TS-SEFFNet [26] 2021 74.71% 0.6628 MRGF [46] 2022 70.11% 0.6015 MI-DABAN [27] 2023 76.16% 0.6821 SCPGE [47] 2023 68.64% 0.5817 Our Method 2024 77.89% 0.7052 Ang et al [13] decomposed the original EEG signals into multiple frequency bands using filter banks and then extracted the features using the common spatial pattern (CSP) method, achieving an accuracy of 67.75% and a Kappa value of 0.57 on the BCI-IV-2a database. Kang et al [14] considered the relationship between the covariance matrices of different subjects and obtained a composite covariance matrix by weighted averaging the covariance matrices of subjects in the dataset.…”

Section: Methodsmentioning

confidence: 99%

“…FBCSP [13] 2008 67.75% 0.5700 CCSP [14] 2009 66.51% 0.5535 ConvNet [16] 2017 72.53% 0.6337 EEGNet [17] 2018 74.61% 0.6615 Incep-EEGNet [18] 2020 74.07% 0.6543 TS-SEFFNet [26] 2021 74.71% 0.6628 MRGF [46] 2022 70.11% 0.6015 MI-DABAN [27] 2023 76.16% 0.6821 SCPGE [47] 2023 68.64% 0.5817 Our Method 2024 77.89% 0.7052…”

Section: Year Acc Kappamentioning

confidence: 99%

“…Li et al [27] improved the MI recognition performance of individual subjects by learning the knowledge of multiple subjects through an adversarial-learning method and two non-shared attention blocks for domain alignment, achieving an accuracy of 76.16% and a Kappa value of 0.6821. Ni et al [47] combined graph-embedding projection and sparse constraints into a semi-supervised least squares model and then adaptively learned the similarity matrix and pseudo-label matrix based on manifold learning to obtain the most discriminative patterns, achieving an accuracy of 68.64% and a Kappa value of 0.5817. The discussed literature highlights the excellent accuracy and Kappa value of the proposed MST-DGCN in MI recognition, which is of great significance to the development of MI-BCI.…”

Section: Year Acc Kappamentioning

confidence: 99%

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MST-DGCN: A Multi-Scale Spatio-Temporal and Dynamic Graph Convolution Fusion Network for Electroencephalogram Recognition of Motor Imagery

Chen,

Liu,

2024

Electronics

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The motor imagery brain-computer interface (MI-BCI) has the ability to use electroencephalogram (EEG) signals to control and communicate with external devices. By leveraging the unique characteristics of task-related brain signals, this system facilitates enhanced communication with these devices. Such capabilities hold significant potential for advancing rehabilitation and the development of assistive technologies. In recent years, deep learning has received considerable attention in the MI-BCI field due to its powerful feature extraction and classification capabilities. However, two factors significantly impact the performance of deep-learning models. The size of the EEG datasets influences how effectively these models can learn. Similarly, the ability of classification models to extract features directly affects their accuracy in recognizing patterns. In this paper, we propose a Multi-Scale Spatio-Temporal and Dynamic Graph Convolution Fusion Network (MST-DGCN) to address these issues. In the data-preprocessing stage, we employ two strategies, data augmentation and transfer learning, to alleviate the problem of an insufficient data volume in deep learning. By using multi-scale convolution, spatial attention mechanisms, and dynamic graph neural networks, our model effectively extracts discriminative features. The MST-DGCN mainly consists of three parts: the multi-scale spatio-temporal module, which extracts multi-scale information and refines spatial attention; the dynamic graph convolution module, which extracts key connectivity information; and the classification module. We conduct experiments on real EEG datasets and achieve an accuracy of 77.89% and a Kappa value of 0.7052, demonstrating the effectiveness of the MST-DGCN in MI-BCI tasks. Our research provides new ideas and methods for the further development of MI-BCI systems.

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