{"slug": "bio-mf-low-latency-and-high-fidelity-eeg-to-fnirs-cross-modal-generation-for", "title": "Bio-MF: Low-Latency and High-Fidelity EEG-to-fNIRS Cross-Modal Generation for Hybrid Motor-Imagery Brain--Computer Interfaces", "summary": "Researchers introduced Bio-MF, a latent-free one-step MeanFlow framework for EEG-conditioned fNIRS generation, detailed in arXiv paper 2609.20904v1. On Dataset 1, EEG plus synthetic fNIRS improved accuracy over EEG-only by 3.37 and 4.15 percentage points for HbR and HbO respectively, while Dataset 2 gains were 2.98 and 2.50 percentage points under an unseen 64-channel EEG montage. Bio-MF generates one fNIRS trial in 7.0 ms on an RTX PRO 6000 GPU, an 857x speedup over the 1000-step SCDM latency, with code available at https://github.com/psychosiwa/Bio-MF.", "body_md": "arXiv:2609.20904v1 Announce Type: new \nAbstract: Hybrid motor-imagery brain-computer interfaces (MI-BCIs) combining EEG and fNIRS can outperform EEG-only systems by exploiting complementary electrophysiological and hemodynamic information. To obtain such hybrid information when paired EEG-fNIRS acquisition is unavailable or inconvenient, recent studies have focused on EEG-to-fNIRS cross-modal generation. However, existing methods still suffer from slow generation and often require pretraining, limiting their use in real-time MI-BCI scenarios. Although one-step generative models offer an attractive route to low-latency synthesis, removing the iterative refinement process can reduce generation fidelity and introduce non-physiological artifacts. To address these problems, this paper proposes Bio-MF, a latent-free one-step MeanFlow framework for EEG-conditioned fNIRS generation. Bio-MF performs direct signal-space x-prediction, converts this signal-space output into MeanFlow velocity supervision, and completes inference with one network evaluation. To preserve task-relevant hemodynamic structure under heterogeneous sensor layouts, Bio-MF integrates Spatial-Temporal Interactive 4D Encoding, cross-modal classifier-free guidance, and noise-level-gated FFT regularization. On Dataset 1, EEG + synthetic fNIRS improves ACC over EEG-only by 3.37 and 4.15 percentage points for HbR and HbO, respectively. On Dataset 2, the corresponding gains remain 2.98 and 2.50 percentage points under the unseen 64-channel EEG montage. On an RTX PRO 6000 GPU, Bio-MF generates one fNIRS trial in 7.0 ms, corresponding to an 857x speedup over the 1000-step SCDM latency. These results show that Bio-MF enables fast EEG-to-fNIRS synthesis while preserving task-relevant generation quality for downstream hybrid MI decoding. Our code is available at https://github.com/psychosiwa/Bio-MF.", "url": "https://wpnews.pro/news/bio-mf-low-latency-and-high-fidelity-eeg-to-fnirs-cross-modal-generation-for", "canonical_source": "https://www.machinebrief.com/news/bio-mf-low-latency-and-high-fidelity-eeg-to-fnirs-cross-moda-wpok", "published_at": "2026-09-21 04:00:00+00:00", "updated_at": "2026-09-21 04:24:54.138968+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "neural-networks"], "entities": ["Bio-MF", "arXiv", "RTX PRO 6000", "SCDM", "MeanFlow"], "alternates": {"html": "https://wpnews.pro/news/bio-mf-low-latency-and-high-fidelity-eeg-to-fnirs-cross-modal-generation-for", "markdown": "https://wpnews.pro/news/bio-mf-low-latency-and-high-fidelity-eeg-to-fnirs-cross-modal-generation-for.md", "text": "https://wpnews.pro/news/bio-mf-low-latency-and-high-fidelity-eeg-to-fnirs-cross-modal-generation-for.txt", "jsonld": "https://wpnews.pro/news/bio-mf-low-latency-and-high-fidelity-eeg-to-fnirs-cross-modal-generation-for.jsonld"}}