cfspmnet-eeg-motor-imagery-stroke

name	cfspmnet-eeg-motor-imagery-stroke
description	CFSPMNet - Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients. Use when working with MI-EEG decoding, cross-subject BCI for stroke rehabilitation, Mamba-based EEG models, or Fourier-domain token reorganization for neural decoding.
license	Complete terms in LICENSE.txt
metadata	{"arxiv_id":"2605.10111","published":"2026-05-11","authors":"Xiangkai Wang, Yun Zhao, Dongyi He, Qingling Xia, Gen Li, Xinlai Xing, Yuchi Pan, Bin Jiang","tags":["eeg","motor-imagery","mamba","stroke-rehabilitation","bci","cross-subject"]}

CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients

arXiv:2605.10111 | Submitted 11 May 2026 | cs.LG, cs.AI, cs.CV

Core Concept

CFSPMNet addresses the challenge of cross-patient MI-EEG decoding for stroke rehabilitation. Pathological neural reorganization makes source-learned MI representations unreliable for unseen patients. CFSPMNet models post-stroke MI-EEG as latent neural-state organization, combining a Fourier-Reorganized State Mamba Network (FRSM) with Shared-Private Prototype Matching (SPPM) for robust cross-subject adaptation.

Key Insights

1. Fourier-Reorganized State Mamba Network (FRSM): Represents each trial as a latent physiological token sequence, reorganizes token states in the Fourier domain, and uses Fourier-derived trial context to guide Mamba state-space propagation. This captures both band-specific spectral patterns and cross-frequency interactions.

2. Shared-Private Prototype Matching (SPPM): Improves target-domain pseudo-label updating by combining semantic confidence with shared-private physiological consistency, filtering confident but physiologically inconsistent target predictions.

3. Leave-One-Subject-Out Results: Achieves 68.23% on XW-Stroke and 73.33% on 2019-Stroke datasets, outperforming CNN, Transformer, Mamba, and adaptation-based baselines with improvements of 5.63 and 8.25 percentage points.

4. Neurophysiological Interpretability: Ablation, sensitivity, feature-alignment, pseudo-label selection, and neurophysiological visualization analyses confirm that Fourier-domain token-state reorganization and calibrated pseudo-label updating contribute to the performance gains.

Method Components

Fourier-Reorganized State Mamba Network

• Encodes EEG trials as latent physiological token sequences
• Reorganizes token states in the Fourier domain to capture spectral structure
• Fourier-derived trial context guides Mamba state-space propagation
• Captures both band-specific and cross-frequency interactions

Shared-Private Prototype Matching

• Maintains shared prototypes (common across subjects) and private prototypes (subject-specific)
• Pseudo-label selection filters based on both semantic confidence and physiological consistency
• Prevents propagation of confident but neurophysiologically implausible predictions

Applications

• Stroke rehabilitation BCI with MI-EEG decoding
• Cross-subject EEG decoding where training data comes from different patients
• Mamba-based neural signal processing for time-series EEG
• Domain adaptation for pathological EEG affected by neural reorganization
• Fourier-domain EEG feature extraction for spectral representation learning

Activation Keywords

• CFSPMNet
• EEG motor imagery decoding
• Mamba EEG network
• Fourier-guided EEG
• cross-subject BCI stroke
• shared-private prototype matching
• stroke rehabilitation EEG
• Fourier domain token reorganization
• Mamba state space EEG
• MI-EEG cross-patient

References

• Wang et al. (2026). CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients. arXiv:2605.10111