2

SzCORE: Seizure Community Open-Source Research Evaluation Framework for EEG-Based Seizure Detection
Community-driven framework standardizing datasets, metrics, and tooling for reproducible EEG seizure detection research.
Jonathan Dan, Una Pale, Alireza Amirshahi, William Cappelletti, Thorir Mar Ingolfsson, Xiaying Wang, Andrea Cossettini, Adriano Bernini, Luca Benini, Sándor Beniczky, David Atienza, Philippe Ryvlin
PDF Code Dataset Open Access PDF DOI (Epilepsia) SzCORE GitHub
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
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
A Muscle Pennation Angle Estimation Framework From Raw Ultrasound Data for Wearable Biomedical Instrumentation
Ultrasound-based pennation estimation with 1.6° RMSE and 11 kB model running in 1.31 ms on GAP9.
Sergei Vostrikov, Thorir Mar Ingolfsson, Soley Hafthorsdottir, Christoph Leitner, Michele Magno, Luca Benini, Andrea Cossettini
Cite IEEE Xplore
A Muscle Pennation Angle Estimation Framework From Raw Ultrasound Data for Wearable Biomedical Instrumentation
Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUs
Compact transformer seizure detector that achieves 73% sensitivity with 0.15 FP/h and runs in 13.7 ms at 0.31 mJ on GAP9.
Paola Busia, Andrea Cossettini, Thorir Mar Ingolfsson, Simone Benatti, Alessio Burrello, Victor J. B. Jung, Moritz Scherer, Matteo A. Scrugli, Adriano Bernini, Pauline Ducouret, Philippe Ryvlin, Paolo Meloni, Luca Benini
PDF Cite Dataset IEEE Xplore CHB-MIT Dataset
Reducing False Alarms in Wearable Seizure Detection With EEGformer: A Compact Transformer Model for MCUs
Minimizing Artifact-Induced False Alarms for Seizure Detection in Wearable EEG Devices with Gradient-Boosted Tree Classifiers
Gradient-boosted seizure + artifact detection that cuts false alarms by 96% while enabling 300-hour wearable monitoring.
Thorir Mar Ingolfsson, Simone Benatti, Xiaying Wang, Adriano Bernini, Pauline Ducouret, Philippe Ryvlin, Sándor Beniczky, Luca Benini, Andrea Cossettini
PDF Cite Dataset Scientific Reports Article CHB-MIT Dataset
Minimizing Artifact-Induced False Alarms for Seizure Detection in Wearable EEG Devices with Gradient-Boosted Tree Classifiers