Fig 1.
The architecture of RMETNet.
Table 1.
RMETNet Structural Details.
Fig 2.
The training process of RMETNet.
Fig 3.
The architecture of TSLANet.
Table 2.
The summary of the BCICIV2a and BCICIV2b datasets used in this paper.
Fig 4.
Visualization of representative motor-imagery trials from the BCICIV2a and BCICIV2b datasets.
The top panel shows BCICIV2a and the bottom panel shows BCICIV2b; columns correspond to subjects 1, 5, and 9. For BCICIV2a, rows represent left-hand, right-hand, feet, and tongue imagery, respectively; for BCICIV2b, rows represent left-hand and right-hand imagery. In each subpanel, the left plot shows EEG waveforms from channels C3, Cz, and C4 over the trial interval from 0.5 to 4.5 s, whereas the right plot shows the scalp topography computed from the same interval using band power in the 8-30 Hz range. Topographic values are expressed as z-scored log10 band power.
Table 3.
Subject-dependent classification results on the BCICIV2a dataset. The best results are highlighted in bold.
Table 4.
Cross-subject classification results on the BCICIV2a dataset. The best results are highlighted in bold.
Table 5.
Subject-dependent classification results on the BCICIV2b dataset. The best results are highlighted in bold.
Table 6.
Cross-subject classification results on the BCICIV2b dataset. The best results are highlighted in bold.
Table 7.
Ablation study results on the BCICIV2a and BCICIV2b datasets.
Fig 5.
UMAP visualization of the test features for subject S1 on BCICIV2a under different settings.
(a) Original test features. (b) Features without the Riemannian branch. (c) Features without MMD-loss-based training. (d) Features produced by the complete model.