activity
20192026
most citedAn Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing

119 citations · 286 across the 27 of their papers we have counts for

collaborators
Showing 2024Show all

6 papers · 1 filter

eess.SP2024

EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation

Luca Benfenati, Sofia Belloni, Alessio Burrello +6

Heart rate (HR) estimation from photoplethysmography (PPG) signals is a key feature of modern wearable devices for health and wellness monitoring. While deep learning models show p…

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)…

eess.SP2024

Optimization and Deployment of Deep Neural Networks for PPG-based Blood Pressure Estimation Targeting Low-power Wearables

Alessio Burrello, Francesco Carlucci, Giovanni Pollo +5

PPG-based Blood Pressure (BP) estimation is a challenging biosignal processing task for low-power devices such as wearables. State-of-the-art Deep Neural Networks (DNNs) trained fo…

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…