Test-Time Adaptation for Cross-Subject Motor Imagery EEG Classification Using Information-Aggregation and Source-Guided Weighting

Individual-specific calibration is a major bottleneck in motor imagery (MI) electroencephalogram (EEG) decoding, limiting real-world neural-feedback rehabilitation. Transfer learning, particularly Test-Time Adaptation (TTA), offers a promising solution for direct online cross-subject adaptation, handling sequentially arriving unlabeled MI-EEG data. However, existing TTA methods, primarily designed for domains such as computer vision, face challenges when applied to MI-EEG data due to its scarcity and non-stationary nature. To address the challenges in direct online MI-EEG decoding, this paper proposes MI-IASW, a novel framework combining Information-Aggregation (IA) and Source-Guided Pseudo-Label Weighting (SW). IA leverages Mixed and Adaptive Batch Normalization (MABN) to ensure effective aggregation of statistical and gradient information. Additionally, IA adopts a Weight Aggregation (WA) strategy to improve generalization under limited data. Meanwhile, SW first evaluates the overconfident pseudo-labels with the guidance of source centers and then employs Class-Aware Weighting (CAW) to adjust sample contributions to the loss function. Experimental evaluations on two public MI-EEG datasets demonstrate that our proposed framework outperforms various competitive baselines, achieving an average performance gain of 3.17% over the baseline TTA methods and 6.80% over the source model. By eliminating the need for individual-specific offline calibration, MI-IASW enables practical deployment in real-world rehabilitation and improves cross-subject decoding.