An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities

Abstract

This paper presents a wearable brain–machine interface that couples a compact convolutional neural network with continual learning to cope with inter-session variability. Across two in-house datasets, the on-device continual-learning workflow improves accuracy by 30.36% and 10.17% compared to static models. Implemented on the BioGAP platform with a GAP9 microcontroller, inference consumes 0.45 mJ per pass while adaptation steps run in 21.5 ms, enabling approximately 25 hours of operation from a 100 mAh battery.

Thorir Mar Ingolfsson

Postdoctoral Researcher

I develop efficient machine learning systems for biomedical wearables that operate under extreme resource constraints. My work bridges foundation models, neural architecture design, and edge deployment to enable real-time biosignal analysis on microwatt-scale devices.