activity
20232026
most citedSzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms

42 citations · 53 across the 21 of their papers we have counts for

collaborators

26 papers

eess.SY2026

Towards an Earpiece for Wearable and Dry Ultrasonic Vagus Nerve Stimulation

Sebastian Frey, Farouk Ghezraoui, Marc Nölke +3

Ultrasound (US)-based auricular vagus nerve stimulation (aVNS) is emerging as a versatile, noninvasive modality for neuromodulation - spanning clinical therapies (e.g., pain manage…

cs.AR2026

Open-Source Multi-Wire SPI Readout for Wearable Ultrasound Probes

Federico Villani, Soumyo Bhattacharjee, Lisa Odermatt +3

Wearable ultrasound probes must transfer increasingly large acquisition payloads while maintaining compact, low-power electronics. In TinyProbe, the current bottleneck in data tran…

eess.SP2026

Open-Source Live-Reconfigurable Multi-Mode Wearable Ultrasound

Cédric Hirschi, Federico Villani, Luca Benini +1

Wearable ultrasound enables continuous deep-tissue monitoring, and a single programmable probe can operate in multiple complementary modes, such as structural A-mode and Doppler fl…

eess.SY2026

WULPUS PRO: Multi-mode Ultra-Low-Power Wearable Ultrasound and Array Imaging with CMUT Support

Sergei Vostrikov, Federico Villani, Cedric Hirschi +7

Wearable ultrasound enables continuous monitoring of physiological processes such as muscle dynamics, bladder volume, and cardiovascular activity. Existing fully wearable ultra-low…

eess.SP2026

Towards Whole Hand and Wrist Kinematic Tracking with a Wearable A-Mode Ultrasound Probe

Giusy Spacone, Luca Benini, Andrea Cossettini

A-mode ultrasound (US) has emerged as a promising modality for hand and wrist motion tracking. Prior works have mainly addressed static gesture classification or regression of a fe…

eess.SP2026

Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU

Elisa Vasta, Thorir Mar Ingolfsson, Andrea Cossettini +4

Burst suppression (BS) is a clinically relevant electroencephalographic (EEG) pattern used to monitor sedation depth and brain activity in critically ill patients, particularly dur…

eess.SY2026

A Wearable Multimodal Ultrasound+Inertial System for Real-Time Virtual Reality Interaction

Giusy Spacone, Sebastian Frey, Enzo Baraldi +3

A-mode ultrasound (US) is a promising sensing modality for Virtual Reality (VR) interaction, as it enables the mapping of muscular activity into control commands while retaining th…

eess.SY2026

Polymer-based Capacitive Micromachined Transducer-Enabled Inline Monitoring of Ultrasonic Welding in Thermoplastic Carbon Fiber Composites

Jonas Welsch, Dominik Goerick, Martin Angerer +8

Thermoplastic composite structures enable lightweight, recyclable, and high-throughput aerospace manufacturing, but reliable quality assurance of advanced joining processes remains…

eess.SP2026

SilentWear: an Ultra-Low Power Wearable System for EMG-based Silent Speech Recognition

Giusy Spacone, Sebastian Frey, Giovanni Pollo +5

Detecting speech from biosignals is gaining increasing attention due to the potential to develop human-computer interfaces that are noise-robust, privacy-preserving, and scalable f…

eess.SY2026

Validation of a Software-Defined 100-Gb/s RDMA Streaming Architecture for Ultrafast Optoacoustic and Ultrasound Imaging

Federico Villani, Christian Vogt, Luca Specht +5

Optoacoustic (OA) imaging has emerged as a powerful investigation tool, with demonstrated applicability in oncology, neuroscience, and cardiovascular biology. However, its clinical…

eess.SP2025

Wearable and Ultra-Low-Power Fusion of EMG and A-Mode US for Hand-Wrist Kinematic Tracking

Giusy Spacone, Sebastian Frey, Mattia Orlandi +5

Hand gesture recognition based on biosignals has shown strong potential for developing intuitive human-machine interaction strategies that closely mimic natural human behavior. In…

eess.SY2025

A Parallel Ultra-Low Power Silent Speech Interface based on a Wearable, Fully-dry EMG Neckband

Fiona Meier, Giusy Spacone, Sebastian Frey +2

We present a wearable, fully-dry, and ultra-low power EMG system for silent speech recognition, integrated into a textile neckband to enable comfortable, non-intrusive use. The sys…

cs.AR2025★ 1 cited

ListenToJESD204B: A Lightweight Open-Source JESD204B IP Core for FPGA-Based Ultrasound Acquisition systems

Soumyo Bhattacharjee, Federico Villani, Christian Vogt +2

The demand for hundreds of tightly synchronized channels operating at tens of MSPS in ultrasound systems exceeds conventional low-voltage differential signaling links' bandwidth, p…

eess.SY2025

BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing

Sebastian Frey, Giusy Spacone, Andrea Cossettini +4

The growing demand for continuous physiological monitoring and human-machine interaction in real-world settings calls for wearable platforms that are flexible, low-power, and capab…

eess.SP2025

Real-Time, Single-Ear, Wearable ECG Reconstruction, R-Peak Detection, and HR/HRV Monitoring

Carlos Santos, Sebastian Frey, Andrea Cossettini +2

Biosignal monitoring, in particular heart activity through heart rate (HR) and heart rate variability (HRV) tracking, is vital in enabling continuous, non-invasive tracking of phys…

eess.SP2024

Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine Interfaces

Lan Mei, Cristian Cioflan, Thorir Mar Ingolfsson +4

Brain-machine interfaces (BMIs) are expanding beyond clinical settings thanks to advances in hardware and algorithms. However, they still face challenges in user-friendliness and s…

eess.SP2024★ 5 cited

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

Lan Mei, Thorir Mar Ingolfsson, Cristian Cioflan +4

Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs)…

cs.RO2024★ 1 cited

GAP9Shield: A 150GOPS AI-capable Ultra-low Power Module for Vision and Ranging Applications on Nano-drones

Hanna Müller, Victor Kartsch, Luca Benini

The evolution of AI and digital signal processing technologies, combined with affordable energy-efficient processors, has propelled the development of both hardware and software fo…

cs.LG2024★ 1 cited

BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy Monitoring

Luca Benfenati, Thorir Mar Ingolfsson, Andrea Cossettini +3

This study presents a novel approach for EEG-based seizure detection leveraging a BERT-based model. The model, BENDR, undergoes a two-phase training process. Initially, it is pre-t…

eess.SP2024

GAPses: Versatile smart glasses for comfortable and fully-dry acquisition and parallel ultra-low-power processing of EEG and EOG

Sebastian Frey, Mattia Alberto Lucchini, Victor Kartsch +7

Recent advancements in head-mounted wearable technology are revolutionizing the field of biopotential measurement, but the integration of these technologies into practical, user-fr…

eess.SP2024★ 1 cited

A Spiking Neural Network Decoder for Implantable Brain Machine Interfaces and its Sparsity-aware Deployment on RISC-V Microcontrollers

Jiawei Liao, Oscar Toomey, Xiaying Wang +4

Implantable Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation, and they demand accurate and energy-efficient algorithms. In this pape…

eess.SP2024★ 42 cited

SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms

Jonathan Dan, Una Pale, Alireza Amirshahi +9

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-ter…

eess.SY2023

A Wearable Ultra-Low-Power sEMG-Triggered Ultrasound System for Long-Term Muscle Activity Monitoring

Sebastian Frey, Victor Kartsch, Christoph Leitner +4

Surface electromyography (sEMG) is a well-established approach to monitor muscular activity on wearable and resource-constrained devices. However, when measuring deeper muscles, it…

cs.AR2023★ 2 cited

Spatz: Clustering Compact RISC-V-Based Vector Units to Maximize Computing Efficiency

Matteo Perotti, Samuel Riedel, Matheus Cavalcante +1

The ever-increasing computational and storage requirements of modern applications and the slowdown of technology scaling pose major challenges to designing and implementing efficie…