BiGMNet: Bidirectional Representations with Predictive Supervision for EEG-Guided Target Speaker Extraction

Brain-assisted target speaker extraction aims to recover the attended target speech from competing speech, using electroencephalography (EEG) as the sole auxiliary cue. However, architectures relying on local post-fusion mappings and waveform supervision lack explicit coupling between temporal organization and direct supervision of target-speech dynamics, while uniform elementwise updates overlook the distinct roles of internal matrices and other parameters. To address these issues, this paper proposes Bidirectional Griffin Adapter with Multi-Frame Prediction Network (BiGMNet), an EEG-conditioned encoder–mask–decoder framework for target speaker extraction. The method introduces three coordinated components: the Bidirectional Griffin Adapter (BiGriffinAdapter) for bidirectional temporal refinement, direction-aware multi-frame prediction (DA-MFP) for supervising directional intermediate states, and Hybrid Muon–AdamW optimization for parameter-role-aware updates. BiGriffinAdapter combines bidirectional gated recurrence and local attention to refine fused representations for mask estimation, and training-only DA-MFP supervises its pre-concatenation forward and backward states through sequential prediction of future and past continuous clean-speech latents, respectively. Hybrid Muon–AdamW approximately orthogonalizes updates to selected internal matrices and applies elementwise adaptive updates to the remaining parameters according to their computational roles. This paper evaluates subject-specific models on MM-AAD and AVED with ten participants per dataset through EEG-only baseline comparisons and component ablations. BiGMNet achieves 9.76 and 10.90 dB SI-SDR, exceeding IFENet's reported scores by 1.57 and 2.14 dB, respectively, with 12.31 G MACs, 15.5% fewer than IFENet. These results support directly supervising EEG-conditioned temporal states to improve target speaker extraction, with the optimization rule chosen according to parameter roles.