MANAS-2：面向脑电图基础模型的约束重建
MANAS-2: Constrained Reconstruction for EEG Foundation Models
- Mannas AI
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
MANAS-2提出结合原始-频带混合掩码自编码器与受物理启发的约束重建正则化器，通过惩罚重建波形RMS能量差异塑造更频谱组织化的潜在空间，在多个EEG数据集上提升下游迁移性能。
中文摘要 AI 辅助
掩码重建被广泛用于脑电图（EEG）基础模型，但在低信噪比波形上优化重建并不一定能产生最有用的潜在表示。我们引入了MANAS-2，一种新的EEG基础模型，它将原始-频带混合（RBH）掩码自编码器与约束重建（ConRec）相结合，后者是一种受物理学启发的正则化器。RBH联合重建时间波形片段和紧凑的频谱带目标，而ConRec仅作用于时间解码器输出，惩罚重建波形相邻短窗口之间RMS能量的差异。ConRec旨在通过使编码器偏向振荡包络信息的组织来塑造编码器。在七个保留的EEG数据集上，将ConRec添加到原本相同的RBH模型中，将六频带频谱功率的冻结岭回归恢复从平均R^2=0.860提高到0.906，将片段间频带能量动态的恢复从R^2=0.283提高到0.354，而时间波形信息仍可从冻结潜在表示中高度恢复。当应用于仅时间的掩码自编码器时，ConRec也改善了冻结下游迁移和频率相关的潜在几何结构，尽管没有接收频谱目标：即ConRec的效果与架构无关。MANAS-2在大多数下游知识迁移任务上也优于领先的EEG基础模型。从ConRec的效果中，我们看到通过解码器施加的物理动机约束可以使潜在空间更具频谱组织性和可迁移性。因此，MANAS-2提供了一种新的EEG基础模型，围绕约束重建作为塑造表示质量（而非重建质量）的机制。
英文摘要
Masked reconstruction is widely used for EEG foundation models, but optimizing reconstruction on low-SNR waveforms does not necessarily produce the most useful latent representation. We introduce MANAS-2, a new EEG foundation model that combines a Raw-Band Hybrid (RBH) masked autoencoder with Constrained Reconstruction (ConRec), a physics-motivated regularizer. RBH jointly reconstructs temporal waveform patches and compact spectral-band targets, while ConRec acts only on the temporal decoder output, penalizing differences in RMS energy between adjacent short windows of the reconstructed waveform. ConRec is intended to shape the encoder by biasing it toward the organization of oscillatory-envelope information. Across seven held-out EEG datasets, adding ConRec to an otherwise identical RBH model increases frozen ridge recovery of six-band spectral power from mean R^2=0.860 to 0.906 and recovery of inter-patch band-energy dynamics from R^2=0.283 to 0.354, while temporal waveform information remains highly recoverable from the frozen latents. Applied to a temporal-only masked autoencoder, ConRec also improves frozen downstream transfer and frequency-dependent latent geometry despite receiving no spectral targets: i.e., the effects of ConRec are architecture-independent. MANAS-2 also outperforms leading EEG Foundation Models on most downstream knowledge-transfer tasks. From the effects of ConRec, we see that a physically motivated constraint imposed through the decoder can make for a more spectrally organized and transferable latent space. MANAS-2 therefore provides a new EEG foundation model built around constrained reconstruction as a mechanism for shaping representation--rather than reconstruction--quality.
