Search Results for author:
Found 7 papers, 1 papers with code
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet
In this paper, we first present a review of the most recent studies using deep learning for MI classification, with particular attention to their cross-subject performance.
ACTA: A Mobile-Health Solution for Integrated Nudge-Neurofeedback Training for Senior Citizens
Beyond SENIOR, ACTA represents a highly-usable, accessible, low-cost, new-generation mobile-health solution to promote independent aging and effective motor-cognitive training support, while empowering the elderly in their own aging.
Comparison of Attention-based Deep Learning Models for EEG Classification
Conclusions: with this work, we shed light over the role of different attention mechanisms in the classification of normal and abnormal EEG patterns.
REPAC: Reliable estimation of phase-amplitude coupling in brain networks
Recent evidence has revealed cross-frequency coupling and, particularly, phase-amplitude coupling (PAC) as an important strategy for the brain to accomplish a variety of high-level cognitive and sensory functions.
Deep learning-based classification of fine hand movements from low frequency EEG
The classification of different fine hand movements from EEG signals represents a relevant research challenge, e. g., in brain-computer interface applications for motor rehabilitation.
Feature selection for gesture recognition in Internet-of-Things for healthcare
Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness.
Deep Learning Techniques for Improving Digital Gait Segmentation
Wearable technology for the automatic detection of gait events has recently gained growing interest, enabling advanced analyses that were previously limited to specialist centres and equipment (e. g., instrumented walkway).
