Self-supervised Learning for Electroencephalogram: A Systematic Survey
Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals.
Integrating supervised deep learning techniques with EEG signals has recently facilitated …
Integrating supervised deep learning techniques with EEG signals has recently facilitated …
[HTML][HTML] A Taxonomy of Low-Power Techniques in Wearable Medical Devices for Healthcare Applications
Chronic diseases are the most prevalent and non-communicable health crisis globally. Most
chronic disease patients require continuous physiological monitoring, using wearable …
chronic disease patients require continuous physiological monitoring, using wearable …
LightFF: Lightweight inference for forward-forward algorithm
The human brain performs tasks with an outstanding energy efficiency, ie, with
approximately 20 Watts. The state-of-the-art Artificial/Deep Neural Networks (ANN/DNN), on …
approximately 20 Watts. The state-of-the-art Artificial/Deep Neural Networks (ANN/DNN), on …
Lightweight Inference for Forward-Forward Training Algorithm
The human brain performs tasks with an outstanding energy-efficiency, ie, with
approximately 20 Watts. The state-of-the-art Artificial/Deep Neural Networks (ANN/DNN), on …
approximately 20 Watts. The state-of-the-art Artificial/Deep Neural Networks (ANN/DNN), on …
Privacy-Preserving Federated Interpretability
Interpretability has become a crucial component in the Machine Learning (ML) domain. This
is particularly important in the context of medical and health applications, where the …
is particularly important in the context of medical and health applications, where the …
Energy-Aware Integrated Neural Architecture Search and Partitioning for Distributed Internet of Things (IoT)
Internet of Things (IoT) are one of the key enablers of personalized health. However, IoT
devices often have stringent constraints in terms of resources, eg, energy budget, and …
devices often have stringent constraints in terms of resources, eg, energy budget, and …
EpilConNet: A Novel Multi-Class Epileptic Seizure Classification Model.
S Ghosh - Inteligencia Artificial: Revista Iberoamericana …, 2024 - search.ebscohost.com
Epilepsy is a neurological disorder characterized by recurrent seizures, which can affect
individuals of all age groups, but infants and older individuals are particularly vulnerable …
individuals of all age groups, but infants and older individuals are particularly vulnerable …
EpilConNet: A Novel Multi-Class Epileptic Seizure Classification Model through Layer Concatenation
S Ghosh, S Gupta - 2024 - researchsquare.com
Epilepsy is a neurological disorder characterized by recurrent seizures, which can affect
individuals of all age groups, but infants and older individuals are particularly vulnerable …
individuals of all age groups, but infants and older individuals are particularly vulnerable …