Concept
Intraoperative neurophysiological monitoring
Publications associées (215)
Who says you are so sick? An investigation on individual susceptibility to cybersickness triggers using EEG, EGG and ECG
In this research paper, we conducted a study to investigate the connection between three objective measures: Electrocardiogram(ECG), Electrogastrogram (EGG), and Electroencephalogram (EEG), and individuals' susceptibility to cybersickness. Our primary obje ...
2024FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems
David Atienza Alonso, Amir Aminifar, Alireza Amirshahi, José Angel Miranda Calero, Jonathan Dan
The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...
2024Resource-Efficient Continual Learning for Personalized Online Seizure Detection
David Atienza Alonso, Giovanni Ansaloni, José Angel Miranda Calero, Jonathan Dan, Amirhossein Shahbazinia, Flavio Ponzina
Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...
2024SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms
David Atienza Alonso, Alireza Amirshahi, Jonathan Dan, Adriano Bernini, William Cappelletti, Luca Benini, Una Pale
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 algorithms in ...
2024An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG
Silvestro Micera, Michael Lassi
Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals. Approach. EEG recordings of 17 heal ...
IOP Publishing Ltd2023