Fig. 8
From: Temporal convolutional transformer for EEG based motor imagery decoding
Subject-wise test accuracy (%) for three datasets under both within-subject and cross-subject evaluation modes. Panels display results for: (a) BCIC IV-2a (within-subject), (b) BCIC IV-2a (cross-subject), (c) BCIC IV-2b (within-subject), (d) BCIC IV-2b (cross-subject), (e) HGD (within-subject), and (f) HGD (cross-subject). Five models are compared—EEGNet, EEGConformer, CTNet, ATCNet, and the proposed TCFormer. Each bar represents the mean accuracy across multiple runs: five runs for BCIC datasets and three runs for HGD. The black error bars represent the standard deviation across these runs for each subject, reflecting variability in model performance. The tables below each panel summarize the mean accuracy, standard deviation, and average rank across subjects. TCFormer consistently achieves the highest mean accuracy and the lowest average rank, demonstrating superior performance across all datasets.
