A comprehensive review of deep learning power in steady-state visual evoked potentials

ZT Al-Qaysi, AS Albahri, MA Ahmed, RA Hamid… - Neural Computing and …, 2024 - Springer
Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from
diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI …

Wearable Brain–Computer Interfaces Based on Steady-State Visually Evoked Potentials and Augmented Reality: A Review

L Angrisani, P Arpaia, E De Benedetto… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
Brain–computer interfaces (BCIs) are an integration of hardware and software
communication systems that allow a direct communication path between the human brain …

Impact of nutritional factors in blood glucose prediction in type 1 diabetes through machine learning

G Annuzzi, A Apicella, P Arpaia, L Bozzetto… - IEEE …, 2023 - ieeexplore.ieee.org
Type 1 Diabetes (T1D) is an autoimmune disease that affects millions of people worldwide.
A critical issue in T1D patients is the managing of Postprandial Glucose Response (PGR) …

A segmentation-denoising network for artifact removal from single-channel EEG

Y Li, A Liu, J Yin, C Li, X Chen - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
As an important neurorecording technique, electroencephalography (EEG) is often
contaminated by various artifacts, which obstructs subsequent analysis. In recent years …

Performance and usability evaluation of an extended reality platform to monitor patient's health during surgical procedures

P Arpaia, E De Benedetto, L De Paolis, G D'Errico… - Sensors, 2022 - mdpi.com
An extended-reality (XR) platform for real-time monitoring of patients' health during surgical
procedures is proposed. The proposed system provides real-time access to a …

A method for optimizing the artifact subspace reconstruction performance in low-density EEG

A Cataldo, S Criscuolo, E De Benedetto… - IEEE Sensors …, 2022 - ieeexplore.ieee.org
Electroencephalogram (EEG) plays a significant role in the analysis of cerebral activity,
although the recorded electrical brain signals are always contaminated with artifacts. This …

Employment of domain adaptation techniques in SSVEP-based brain–computer interfaces

A Apicella, P Arpaia, E De Benedetto, N Donato… - IEEE …, 2023 - ieeexplore.ieee.org
This work addresses the employment of Machine Learning (ML) and Domain Adaptation
(DA) in the framework of Brain-Computer Interfaces (BCIs) based on Steady-State Visually …

Predicting and monitoring blood glucose through nutritional factors in type 1 diabetes by artificial neural networks

G Annuzzi, L Bozzetto, A Cataldo, S Criscuolo… - Acta IMEKO, 2023 - acta.imeko.org
The monitoring and management of Postprandial Glucose Response (PGR), by
administering an insulin bolus before meals, is a crucial issue in Type 1 Diabetes (T1D) …

Control of the robotic arm system with an SSVEP-based BCI

R Fu, X Feng, S Wang, Y Shi, C Jia… - … Science and Technology, 2024 - iopscience.iop.org
Recent studies on brain–computer interfaces (BCIs) implemented in robotic systems have
shown that the system's effectiveness in assisting individuals with movement disorders to …

Driver Cognitive Architecture Based on EEG Signals: A Review

P Mi, L Yan, Y Cheng, Y Liu, J Wang… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
To improve the driving performance of vehicles, it is of great significance to study the
changes in the driver's brain cognition during driving and to establish an intelligent driving …