Results 81 to 90 of about 305,708 (349)
New Directions for Adult and Continuing Education, EarlyView.ABSTRACT Mental well‐being is central to adult learner success, yet many adult education institutions lack capacity to provide timely and accessible support. This article examines how artificial intelligence (AI) can strengthen mental health–adjacent supports in adult and continuing higher education, with attention to professional practice and ...
Adam L. McClain, Thomas Wade
wiley +1 more source
Motor imagery based brain–computer interfaces
, 2018 Publisher Copyright: © 2018 Elsevier Inc. All rights reserved.This chapter is intended as a comprehensive introduction to motor imagery (MI) based brain-computer interface (BCI) systems for readers with sufficient technological background but maybe not ...
Carmen Vidaurre +3 more
core +1 more source
Biased feedback in brain-computer interfaces
Journal of NeuroEngineering and Rehabilitation, 2010 Even though feedback is considered to play an important role in learning how to operate a brain-computer interface (BCI), to date no significant influence of feedback design on BCI-performance has been reported in literature.
Barbero Álvaro, Grosse-Wentrup Moritz
doaj +1 more source
Encoder-decoder optimization for brain-computer interfaces. [PDF]
PLoS Computational Biology, 2015 Neuroprosthetic brain-computer interfaces are systems that decode neural activity into useful control signals for effectors, such as a cursor on a computer screen.
Josh Merel +3 more
doaj +1 more source
World NeurosurgeryBrain-Computer Interfaces (BCIs), a remarkable technological advancement in neurology and neurosurgery, mark a significant leap since the inception of electroencephalography (EEG) in 1924.
W. Awuah +13 more
semanticscholar +1 more source
Brain–computer interfaces for communication and rehabilitation
Nature Reviews Neurology, 2016 Brain–computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment.
U. Chaudhary +2 more
semanticscholar +1 more source
IEEE transactions on neural systems and rehabilitation engineering, 2020 In recent years, deep-learning models gained attention for electroencephalography (EEG) classification tasks due to their excellent performance and ability to extract complex features from raw data.
E. Santamaría-Vázquez +3 more
semanticscholar +1 more source
Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces [PDF]
IEEE transactions on neural systems and rehabilitation engineering, 2019 Transfer learning makes use of data or knowledge in one problem to help solve a different, yet related, problem. It is particularly useful in brain-computer interfaces (BCIs), for coping with variations among different subjects and/or tasks.
Wen Zhang, Dongrui Wu
semanticscholar +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Accessing brain magnetic resonance imaging (MRI) can be challenging, especially for underserved patients, which may lead to disparities in neurological diagnosis. Method This mixed‐methods study enrolled adults with one of four neurological disorders: mild cognitive impairment or dementia of the Alzheimer type, multiple sclerosis ...
Maya L. Mastick +19 more
wiley +1 more source
Functional and Structural Evidence of Neurofluid Circuit Aberrations in Huntington Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Disrupted neurofluid regulation may contribute to neurodegeneration in Huntington disease (HD). Because neurofluid pathways influence waste clearance, inflammation, and the distribution of central nervous system (CNS)–delivered therapeutics, understanding their dysfunction is increasingly important as targeted treatments emerge.
Kilian Hett +8 more
wiley +1 more source