← 研踪 PaperTrailarXiv:2611.01548 · v1
Band2Net: Frequency-to-Network Transport Distillation for fMRI-Privileged, EEG-Only Session-Condition Decoding
首次提交 2026-11-01 12:20
版本更新 2026-11-01 12:20
雷达观测 2026-11-04 23:43
摘要
Functional MRI resolves large-scale brain networks but is expensive and stationary, whereas EEG is portable but poorly localised. We treat fMRI as privileged information, available during training and absent at deployment, with EEG and fMRI acquired from the same participants under matched conditions but not simultaneously. Band2Net encodes EEG into five frequency-specific connectivity graphs and fMRI into seven canonical network tokens from a frozen teacher, and solves a balanced entropic transport problem between the two token sets that gives each EEG band its own fMRI target. In subject-grouped cross-validation on the PsiConnect psilocybin data set, Band2Net reaches 0.801 ROC-AUC for EEG-only session-condition decoding, 5.0 AUC points (95% CI 1.8-8.2) above an EEG-only student. Although its margins over a single shared plan, a KL-relaxed plan and label-free network descriptors remain unresolved, secondary comparisons favour band-specific transported targets over logit, global-feature, contrastive and pooled-target distillation, though not after Holm correction.
作者备注：5 pages, 2 figures, 1 table
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v1 · 2026-11-01 12:20 · 最新观测版本
Band2Net: Frequency-to-Network Transport Distillation for fMRI-Privileged, EEG-Only Session-Condition Decoding
Functional MRI resolves large-scale brain networks but is expensive and stationary, whereas EEG is portable but poorly localised. We treat fMRI as privileged information, available during training and absent at deployment, with EEG and fMRI acquired from the same participants under matched conditions but not simultaneously. Band2Net encodes EEG into five frequency-specific connectivity graphs and fMRI into seven canonical network tokens from a frozen teacher, and solves a balanced entropic transport problem between the two token sets that gives each EEG band its own fMRI target. In subject-grouped cross-validation on the PsiConnect psilocybin data set, Band2Net reaches 0.801 ROC-AUC for EEG-only session-condition decoding, 5.0 AUC points (95% CI 1.8-8.2) above an EEG-only student. Although its margins over a single shared plan, a KL-relaxed plan and label-free network descriptors remain unresolved, secondary comparisons favour band-specific transported targets over logit, global-feature, contrastive and pooled-target distillation, though not after Holm correction.
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