Multi-Scale BiMamba with Test-Time Adaptation for Motor Imagery Classification
Hybrid brain-computer interfaces combining electroencephalography and functional near-infrared spectroscopy utilize neurovascular coupling to improve decoding. Current fusion strategies rely on shallow aggregation or complex attention, which struggle to capture complex temporal dynamics and complex dependencies in long sequences. Moreover, static models lack online calibration for sensor failures or non-stationary noise, causing significant performance drops. We propose the Multi-Scale Bidirectional Mamba with Test-Time Adaptation (MS-BiM-TTA) framework. The architecture employs a multi-scale frontend for heterogeneous feature extraction and bidirectional state-space models for efficient long-range intra-modal and inter-modal modeling with linear complexity. To ensure robustness, a customized test-time adaptation workflow utilizes two-level entropy filtering for update safety and mutual information sharing for autonomous representation reconstruction. Results show that MS-BiM-TTA significantly enhances accuracy and demonstrates superior resilience to modality missingness in both binary and fine-grained tasks.