Results 41 to 50 of about 2,082,534 (353)
Perspectives of the ASHA Special Interest Groups, 2019 Purpose Brain–computer interface (BCI) techniques may provide computer access for individuals with severe physical impairments. However, the relatively hidden nature of BCI control obscures how BCI systems work behind the scenes, making it difficult to understand “how” electroencephalography (EEG) records the BCI-related brain ...
Pitt, Kevin M. +4 more
openaire +4 more sources
Scientific Reports, 2023 Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the interpretability of DL models remains an under-explored area. In
Akshay Sujatha Ravindran +1 more
doaj +1 more source
Frontiers in Human Neuroscience, 2020 Although scalp EEG functional networks have been applied to the study of motor tasks using electroencephalography (EEG), the selection of a suitable reference electrode has not been sufficiently researched.
Lipeng Zhang +13 more
doaj +1 more source
Brain-computer interface technology: a review of the first international meeting.
IEEE transactions on rehabilitation engineering, 2000 Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication ...
J. Wolpaw +9 more
semanticscholar +1 more source
Differences in Intersubject Early Readiness Potentials Between Voluntary and Instructed Actions
Frontiers in Psychology, 2020 Readiness potential (RP) is a slow negative electroencephalogram (EEG) potential prior to voluntary action and was first described by Kornhuber and Deecke (1965).
Lipeng Zhang +11 more
doaj +1 more source
Scientific Reports, 2017 Recent advances in non-invasive brain-computer interface (BCI) technologies have shown the feasibility of neural decoding for both users’ gait intent and continuous kinematics. However, the dynamics of cortical involvement in human upright walking with a
Trieu Phat Luu +3 more
doaj +1 more source
Disability and Rehabilitation: Assistive Technology, 2018 We investigated how overt visual attention and oculomotor control influence successful use of a visual feedback brain-computer interface (BCI) for accessing augmentative and alternative communication (AAC) devices in a heterogeneous population of individuals with profound neuromotor impairments.
Jonathan S. Brumberg +3 more
openaire +5 more sources
Protecting privacy of users in brain-computer interface applications [PDF]
, 2019 Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference.
Agarwal, Anisha +6 more
core +2 more sources
Generative Adversarial Networks-Based Data Augmentation for Brain–Computer Interface
IEEE Transactions on Neural Networks and Learning Systems, 2020 The performance of a classifier in a brain–computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled ...
Fatemeh Fahimi +4 more
semanticscholar +1 more source
A note on brain actuated spelling with the Berlin brain-computer interface [PDF]
, 2007 Brain-Computer Interfaces (BCIs) are systems capable of decoding neural activity in real time, thereby allowing a computer application to be directly controlled by the brain.
A. Kübler +18 more
core +5 more sources