ultra-low power

A Wearable Ultra-Low-Power System for EEG-Based Speech-Imagery Interfaces
First fully wearable EEG speech-imagery system delivering 13-class decoding on GAP9 at 25.93 mW with >21 hours autonomy.
Thorir Mar Ingolfsson, Victor Javier Kartsch, Luca Benini, Andrea Cossettini
IEEE Xplore
Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain-Machine Interfaces
On-device continual learning for wearable BMIs that reaches 92% accuracy with 1.6 min recalibration and 1 mJ training steps.
Lan Mei, Cristian Cioflan, Thorir Mar Ingolfsson, Victor Kartsch, Andrea Cossettini, Xiaying Wang, Luca Benini
PDF Code Preprint (arXiv) GitHub Repository DOI
VowelNet: Enhancing Communication with Wearable EEG-Based Vowel Imagery
Lightweight imagined-speech decoder that runs on GAP9 at 17 mW using only eight EEG channels.
Thorir Mar Ingolfsson, Victor Javier Kartsch Morinigo, Andrea Cossettini, Xiaying Wang, Luca Benini
PDF PDF (ETH Research Collection)
GAPses: Versatile Smart Glasses for Comfortable and Fully-Dry Acquisition and Parallel Ultra-Low-Power Processing of EEG and EOG
Fully dry EEG/EOG smart glasses with on-board GAP9 processing delivering 99% biometric accuracy and 12-hour autonomy.
Sebastian Frey, Mattia Alberto Lucchini, Victor Kartsch, Thorir Mar Ingolfsson, Andrea Helga Bernardi, Michael Segessenmann, Jakub Osieleniec, Simone Benatti, Luca Benini, Andrea Cossettini
PDF Preprint (arXiv) DOI
An Ultra-Low Power Wearable BMI System with Continual Learning Capabilities
Continual-learning BMI on GAP9 that improves accuracy by up to 30% while sustaining 25-hour wearable operation.
Lan Mei, Thorir Mar Ingolfsson, Cristian Cioflan, Victor Kartsch, Andrea Cossettini, Xiaying Wang, Luca Benini
PDF Preprint (arXiv) DOI