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Cross-subject EEG feature matrix classification method and its application in brain-computer interface

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Abstract

EEG signals are widely utilized in brain-computer interface (BCI) applications. However, the non-linear and non-stationary nature of EEG signals poses a challenge when dealing with variations across subjects and sessions, leading to the covariate shift problem in recognition tasks. Conventional approaches often extract vector-form features for classifying EEG signals on a per-subject and per-session basis, resulting in the loss of discriminative features and decreased recognition performance. To address this issue, this paper presents a novel cross-subject EEG feature matrix classification method that leverages the feature matrix encompassing all subjects to recognize EEG signals. The proposed method begins by aligning the EEG covariances of each subject to an identity distribution, followed by extracting a feature matrix from the aligned EEG signals. To recognize EEG signals associated with specific mental tasks, a sparse support matrix machine is employed to select discriminative features from the feature matrix and perform classification based on these selected features. To evaluate the proposed method, two publicly available benchmark datasets containing motor imagery and event-related potentials were used in experiments. Comparative analyses with state-of-the-art methods demonstrated improved recognition performance with the proposed method. Furthermore, additional ablation studies were conducted to explore the potential application prospects of the proposed method in BCI researches.

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Data availability

There are two public benchmark datasets adopted in this paper, which can be found at:

(1) BCIIV MI-2a dataset: https://www.bbci.de/competition/iv/#datasets

(2) ERN dataset: https://www.kaggle.com/competitions/inria-bci-challenge

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Acknowledgements

This work was funded by the National Natural Science Foundation of China under Grant (No. 62106049), Natural Science Foundation of Fujian Province of China under Grant (No. 2022J01655). The author wants to thank the members of the digital Fujian internet-of-thing laboratory of environmental monitoring in Fujian Normal University. The author is very grateful to the anonymous reviewers for their constructive comments which have helped significantly in revising this work.

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  1. College of Computer and Cyber Security, Fujian Normal University, Fuzhou, 350117, China

    Tian-jian Luo

  2. Digital Fujian Internet-of-Thing Laboratory of Environmental Monitoring, Fujian Normal University, Fuzhou, 361005, China

    Tian-jian Luo

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Correspondence to Tian-jian Luo.

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Luo, Tj. Cross-subject EEG feature matrix classification method and its application in brain-computer interface. Multimed Tools Appl 83, 79627–79646 (2024). https://doi.org/10.1007/s11042-024-18648-4

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