EEG-Based Multi-Frequency Multilayer Network for Exploring the Brain State Evolution Underlying Motor Imagery

Lv, Dongmei; Dang, Weidong; Sun, Xinlin; Gao, Zhong-Ke

doi:10.1109/jetcas.2023.3265928

Cited by 2 publications

(2 citation statements)

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“…Brain network analysis has been used for feature extraction in decoding tasks in MI-BCI systems [46][47][48]. The development of multilayer network theory has provided new insights into the mechanisms of information exchange in the brain during MI tasks [56,64]. However, whether multilayer networks can be used for channel selection in MI-BCI systems remains unclear.…”

Section: Discussionmentioning

confidence: 99%

“…We used the graph learning algorithm to estimate the single-layer network by using EEG data from each trial filtered at frequency bands of 8-10.5 Hz (α1), 10.5-13 Hz (α2), 13-18 Hz (β1), and 18-30 Hz (β2). This range of frequency bands covered the µ and β rhythms, which are the main frequency bands induced by MI signals [56]. Following this, we constructed the multilayer network by using four frequency-specific networks.…”

Section: Multilayer Network-based Channel Selection (Mncs)mentioning

confidence: 99%

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Multilayer network-based channel selection for motor imagery brain–computer interface

Yan,

Hu,

Zhang

et al. 2024

J. Neural Eng.

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Objective: The number of electrode channels in a motor imagery-based brain–computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel. This leads to a loss of the interaction between channels and the exchange of information between networks operating at different frequency bands.Approach: We integrate brain networks containing four frequency bands into a multilayer network framework and propose a multilayer network-based channel selection (MNCS) method for MI-BCI systems. A graph learning-based method is used to estimate the multilayer network from electroencephalogram (EEG) data that are filtered by multiple frequency bands. The multilayer participation coefficient (MPC) of the multilayer network is then computed to select EEG channels that do not contain redundant information. Furthermore, the common spatial pattern (CSP) method is used to extract effective features. Finally, a support vector machine (SVM) classifier with a linear kernel is trained to accurately identify MI tasks. Main results: We used three publicly available datasets from the BCI Competition containing data on 12 healthy subjects and one dataset containing data on 15 stroke patients to validate the effectiveness of our proposed method. The results showed that the proposed MNCS method outperforms all channels (85.8% vs. 93.1%, 84.4% vs. 89.0%, 71.7% vs. 79.4%, and 72.7% vs. 84.0%). Moreover, it achieved significantly higher decoding accuracies on MI-BCI systems than state-of-the-art methods (paired t-tests, p <0.05).Significance: The experimental results showed that the proposed MNCS method can select appropriate channels to improve the decoding performance as well as the convenience of the application of MI-BCI systems.

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