ARNI Working Group 2026 – Foundation Models for Wearables: From EEG and EMG to Multimodal Models at the Edge

Mon, 19 Oct 2026 15:00:00 -0400

An invited talk at the monthly meeting of the “Frontier Models for Neuroscience and Behavior” working group of ARNI, the NSF AI Institute for Artificial and Natural Intelligence led by Columbia University. The session was held online on 19 October 2026 (15:00 US Eastern / 21:00 Zürich) and hosted by Alexandre André of Eva Dyer’s lab at UPenn.

Talk highlights

Three constraints, one research path: electrode layouts, long recordings and local inference as the design inputs for every model in the family.
Objective matters for transfer: masked reconstruction plus LeJEPA latent prediction gave the best transfer to an unseen montage, with the lowest variance across seeds.
One EMG encoder, four tasks: TinyMyo covers gestures, kinematics and speech from a single 3.6M-parameter pre-trained model, with a 1.9M-parameter variant under 100 ms on a microcontroller.
Measured on the chip: every deployment number is a measurement on GAP9 at 370 MHz, including the finding that the model with the fewest FLOPs (FEMBA) has the longest latency.

From the slides

Where we come from: chips become wearables, wearables produce data, and the models go back onto the chip.

The model family: each model answers one of the three constraints, and GAP9 is where all of them have to run.

Slides & resources

Download the slides (PDF) →
BioFoundation codebase →
Model weights on Hugging Face →
ARNI working group speakers page →
NeurIPS 2026 EEG/EMG Foundation Challenge → (mentioned in the talk; I co-organise the BCI track)

ARNI | Thorir Mar Ingolfsson

ARNI Working Group 2026 – Foundation Models for Wearables: From EEG and EMG to Multimodal Models at the Edge

Talk highlights

From the slides

Slides & resources

Related work