arXiv Artificial Intelligence

MANAS-2: Constrained Reconstruction for EEG Foundation Models

1 hour ago 15.09.2026 07:00 👁 1 views
MANAS-2: Constrained Reconstruction for EEG Foundation Models

Quick summary

arXiv:2609.13717v1 Announce Type: new Abstract: 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 ad

Key takeaways

  • arXiv:2609.13717v1 Announce Type: new Abstract: 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 ad

Why it matters

“MANAS-2: Constrained Reconstruction for EEG Foundation Models” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗