Deep learning in motor imagery EEG signal decoding: A Systematic Review
Thanks to the fast evolution of electroencephalography (EEG)-based brain-computer
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …
Enhancing IoT Network Security: Unveiling the Power of Self-Supervised Learning against DDoS Attacks
The Internet of Things (IoT), projected to exceed 30 billion active device connections
globally by 2025, presents an expansive attack surface. The frequent collection and …
globally by 2025, presents an expansive attack surface. The frequent collection and …
Stability of mental motor-imagery classification in EEG depends on the choice of classifier model and experiment design, but not on signal preprocessing
MJ Rosenfelder, M Spiliopoulou… - Frontiers in …, 2023 - frontiersin.org
Introduction Modern consciousness research has developed diagnostic tests to improve the
diagnostic accuracy of different states of consciousness via electroencephalography (EEG) …
diagnostic accuracy of different states of consciousness via electroencephalography (EEG) …
Kcs-fcnet: Kernel cross-spectral functional connectivity network for eeg-based motor imagery classification
DG García-Murillo, AM Álvarez-Meza… - Diagnostics, 2023 - mdpi.com
This paper uses EEG data to introduce an approach for classifying right and left-hand
classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity …
classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity …
Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Imagery and Motor Signals in ECoG-Based BCIs
S Bafana - arXiv preprint arXiv:2311.13507, 2023 - arxiv.org
Motor impairments, frequently caused by neurological incidents like strokes or traumatic
brain injuries, present substantial obstacles in rehabilitation therapy. This research aims to …
brain injuries, present substantial obstacles in rehabilitation therapy. This research aims to …
A Comprehensive Analysis of the Design of Brain-Computer Interface Systems Utilizing Electroencephalography as a Means of Measurement: A Survey
RI Ajel, NM Shati, FA Abdullatif - Ingenierie des Systemes d' …, 2024 - search.proquest.com
With the use of Brain-Computer Interface (BCI) technologies, the brain and the outside world
can communicate directly, bypassing the peripheral nervous system. This concept is …
can communicate directly, bypassing the peripheral nervous system. This concept is …
Supervised dimensionality reduction applications in identifying cognitive correlates of psychiatric symptoms and extracting low-dimensional EEG signals to study the …
AM Chinchani - 2024 - open.library.ubc.ca
In the age of big data and large neuroscience datasets, dimensionality reduction is
employed to reduce the complexity of recorded data and explain it with fewer representative …
employed to reduce the complexity of recorded data and explain it with fewer representative …