Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Cossettini, Andrea"'
Autor:
Mei, Lan, Ingolfsson, Thorir Mar, Cioflan, Cristian, Kartsch, Victor, Cossettini, Andrea, Wang, Xiaying, Benini, Luca
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) to improve portability and privacy and maximize battery life. However,
Externí odkaz:
http://arxiv.org/abs/2409.10654
Autor:
Mei, Lan, Cioflan, Cristian, Ingolfsson, Thorir Mar, Kartsch, Victor, Cossettini, Andrea, Wang, Xiaying, Benini, Luca
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 signal variability. Classification models need periodic adaptation for
Externí odkaz:
http://arxiv.org/abs/2409.09161
Autor:
Benfenati, Luca, Ingolfsson, Thorir Mar, Cossettini, Andrea, Pagliari, Daniele Jahier, Burrello, Alessio, Benini, Luca
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-trained on the extensive Temple University Hospital EEG Corpus (TUEG),
Externí odkaz:
http://arxiv.org/abs/2406.19189
Autor:
Frey, Sebastian, Lucchini, Mattia Alberto, Kartsch, Victor, Ingolfsson, Thorir Mar, Bernardi, Andrea Helga, Segessenmann, Michael, Osieleniec, Jakub, Benatti, Simone, Benini, Luca, Cossettini, Andrea
Recent advancements in head-mounted wearable technology are revolutionizing the field of biopotential measurement, but the integration of these technologies into practical, user-friendly devices remains challenging due to issues with design intrusive
Externí odkaz:
http://arxiv.org/abs/2406.07903
Autor:
Dan, Jonathan, Pale, Una, Amirshahi, Alireza, Cappelletti, William, Ingolfsson, Thorir Mar, Wang, Xiaying, Cossettini, Andrea, Bernini, Adriano, Benini, Luca, Beniczky, Sándor, Atienza, David, Ryvlin, Philippe
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-term EEG monitoring. Heterogeneity in validation methods of these algorit
Externí odkaz:
http://arxiv.org/abs/2402.13005
Brain-machine interfaces (BMIs) have emerged as a transformative force in assistive technologies, empowering individuals with motor impairments by enabling device control and facilitating functional recovery. However, the persistent challenge of inte
Externí odkaz:
http://arxiv.org/abs/2309.07798
A Wearable Ultra-Low-Power sEMG-Triggered Ultrasound System for Long-Term Muscle Activity Monitoring
Autor:
Frey, Sebastian, Kartsch, Victor, Leitner, Christoph, Cossettini, Andrea, Vostrikov, Sergei, Benatti, Simone, Benini, Luca
Surface electromyography (sEMG) is a well-established approach to monitor muscular activity on wearable and resource-constrained devices. However, when measuring deeper muscles, its low signal-to-noise ratio (SNR), high signal attenuation, and crosst
Externí odkaz:
http://arxiv.org/abs/2309.06851
Autor:
Ingolfsson, Thorir Mar, Chakraborty, Upasana, Wang, Xiaying, Beniczky, Sandor, Ducouret, Pauline, Benatti, Simone, Ryvlin, Philippe, Cossettini, Andrea, Benini, Luca
Epilepsy is a prevalent neurological disorder that affects millions of individuals globally, and continuous monitoring coupled with automated seizure detection appears as a necessity for effective patient treatment. To enable long-term care in daily-
Externí odkaz:
http://arxiv.org/abs/2309.07135
Autor:
Frey, Sebastian, Guermandi, Marco, Benatti, Simone, Kartsch, Victor, Cossettini, Andrea, Benini, Luca
Wearable biosignal processing applications are driving significant progress toward miniaturized, energy-efficient Internet-of-Things solutions for both clinical and consumer applications. However, scaling toward high-density multi-channel front-ends
Externí odkaz:
http://arxiv.org/abs/2307.01619
In the context of epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological similarity in both amplitude and frequency, making seizure detection systems susceptible to higher false alarm rates. In this work we pres
Externí odkaz:
http://arxiv.org/abs/2204.09577