RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: BCICIV2a: 71.39% (cross-subject), 80.71% (subject-dependent); BCICIV2b: 80.93% (cross-subject), 86.76% (subject-dependent)

• Primary Outcome: Classification accuracy in cross-subject and subject-dependent MI-EEG decoding

• Nnt Or Sensitivity: Accuracy improvements over baseline methods (specific values not quantified in abstract)

• Confidence Interval: Not reported in abstract

Clinical Application

Requires specialized EEG equipment and computational resources; implementation complexity moderate to high Would require TGA approval for clinical use; potential applications in rehabilitation medicine and assistive technology through NDIS funding pathways Patients requiring brain-computer interface systems for motor control, particularly those with motor disabilities

Abstract

Motor imagery electroencephalogram (MI-EEG) analysis is essential for natural interaction and autonomous control in brain-computer interfaces (BCIs). However, deep learning models often struggle with inter-subject variability, which limits their ability to generalize across subjects. This study proposes RMETNet, a novel framework that integrates TSLANet, a spatio-temporal convolution module, and a multi-scale Riemannian geometry feature module. TSLANet suppresses noise and captures complex temporal patterns for preliminary signal decoding, while the spatio-temporal convolution module extracts higher-order representations. The Riemannian branch learns geometry-based distribution features across subjects, and the fused features are used for classification. To address inter-subject distribution shifts, RMETNet incorporates Maximum Mean Discrepancy (MMD) loss for domain adaptation, aligning feature distributions between source and target domains. Experiments show that on the four-class BCI Competition IV 2a (BCICIV2a) dataset, RMETNet achieved accuracies of 71.39% in the cross-subject setting and 80.71% in the subject-dependent setting; on the two-class BCI Competition IV 2b (BCICIV2b) dataset, it achieved 80.93% and 86.76%, respectively. The model consistently outperformed baseline algorithms. Ablation and visualization analyses further validated its effectiveness in reducing inter-subject feature distribution disparities and enhancing MI-EEG decoding. The code is available at: https://github.com/rokanfeermecer486/RMETNet.

References

1. 1.Zhao, Y., He, D., Ren, F., Xia, Q., Xu, L., Xie, G., Zhang, X., Yang, R., Zou, S., & Jiang, B. (2026). RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features. PLoS One. https://doi.org/10.1016/j.neuroimage.2023.120209