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✦ This Week's Partner: Healthcare Brew

Neurotechnology News | Feb 14–Feb 20, 2026

Let's cut through the noise this week!

This week, a blood biomarker study offered a new way to forecast Alzheimer's symptom onset within a 3–4 year window, and Zyphra open-sourced ZUNA, a 380M-parameter EEG foundation model designed to solve the field's oldest data fragmentation problem. No notable policy signals crossed the threshold this week. Bottom line: one clinical paper and one infrastructure release — both pointing toward a more predictable, scalable noninvasive neurotech stack.

Published: Feb 19, 2026
Read more ➡️

Researchers trained timing models using longitudinal plasma biomarkers (including phosphorylated tau) in a cohort of 600+ older adults, estimating symptom onset within ~3–4 years. If replicated prospectively, this could tighten prevention-trial enrollment and reduce cost by better selecting “time-to-symptoms” candidates.

💡 Big Picture: Neurodiagnostics is shifting from “detect disease” to “forecast trajectory,” which matters for trial design, not just early screening.

Published: Feb 18, 2026
Read more ➡ Zyphra / GitHub Repo / MarkTechPost

Zyphra released ZUNA, a 380-million-parameter masked diffusion autoencoder trained on 208 harmonized public EEG datasets (~2 million channel-hours, ~24 million 5-second samples) that reconstructs, denoises, and upsamples scalp EEG across arbitrary electrode layouts — significantly outperforming the standard MNE spherical-spline interpolation baseline, including under extreme 90% channel dropout (10× upsampling). The model is released open-source under Apache 2.0 with weights on HuggingFace and a full MNE-compatible inference stack, targeting researchers, clinicians, and BCI developers.

💡 Big Picture: By encoding electrode geometry as 4D spatial-temporal coordinates (4D RoPE) rather than fixed montage indices, ZUNA creates a hardware-agnostic EEG representation layer — a potential shared backbone that could standardize upstream preprocessing across the entire noninvasive BCI stack.

ZUNA was trained on 208 public EEG datasets — research data collected under consent agreements that almost certainly said nothing about commercial foundation model training. As EEG foundation models proliferate, who owns the generalizable representation learned from those signals, and do the original participants have any claim over a model that may encode their neural patterns permanently in its weights?

U.S. neural data privacy laws (California, Colorado, Montana) regulate commercial collection and sharing, but none clearly address training corpus construction from archived research data — and the EU GDPR's "compatible purposes" doctrine is untested here. The neurotech community needs this conversation before the next model trains on hundreds of millions of clinical EEG hours.

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