Results 41 to 50 of about 68,213 (166)

Using data from cue presentations results in grossly overestimating semantic BCI performance

Scientific Reports
Neuroimaging studies have reported the possibility of semantic neural decoding to identify specific semantic concepts from neural activity. This offers promise for brain-computer interfaces (BCIs) for communication.
Milan Rybář, Riccardo Poli, Ian Daly
doaj   +1 more source

Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools

Scientific Data
Semantic neural decoding aims to identify which semantic concepts an individual focuses on at a given moment based on recordings of their brain activity.
Milan Rybář, Riccardo Poli, Ian Daly
doaj   +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, Reinhold Scherer, Scherer, Reinhold, Vidaurre, Carmen   +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, Donald M Pianto, John P Cunningham, Liam Paninski   +3 more
doaj   +1 more source

Edge AI-Brain–Computer Interfaces System: A Survey

IEEE Transactions on Neural Systems and Rehabilitation Engineering
Edge artificial intelligence (Edge AI) has emerged as a transformative paradigm for enhancing the performance, portability, and autonomy of brain–computer interface (BCI) systems.
Manh-Dat Nguyen, Thomas Do, Xuan-The Tran, Quoc-Toan Nguyen, Chin-Teng Lin   +4 more
doaj   +1 more source

Design and evaluation of fusion approach for combining brain and gaze inputs for target selection.

Frontiers in Neuroscience, 2016
Gaze-based interfaces and Brain-Computer Interfaces (BCIs) allow for hands-free human-computer interaction. In this paper, we investigate the combination of gaze and brain-computer interfaces.
Andeol Evain, Andeol Evain, Ferran Argelaguet, Gery Casiez, Gery Casiez, Gery Casiez, Nicolas Roussel, Anatole Lecuyer   +7 more
doaj   +1 more source

Electroencephalography-Based Brain–Machine Interfaces in Older Adults: A Literature Review

Bioengineering, 2023
The aging process is a multifaceted phenomenon that affects cognitive-affective and physical functioning as well as interactions with the environment.
Luca Mesin, Giuseppina Elena Cipriani, Martina Amanzio   +2 more
doaj   +1 more source

A Novel 3D Paradigm for Target Expansion of Augmented Reality SSVEP

IEEE Transactions on Neural Systems and Rehabilitation Engineering
Steady-State Visual Evoked Potentials (SSVEP) have proven to be practical in Brain-Computer Interfaces (BCI), particularly when integrated with augmented reality (AR) for real-world application.
Beining Cao, Charlie Li-Ting Tsai, Nan Zhou, Thomas Do, Chin-Teng Lin   +4 more
doaj   +1 more source

An EEG dataset to study neural correlates of audiovisual long-term memory retrieval

Scientific Data
Memory retrieval is a fundamental cognitive process that plays a critical role in our lives. Studying the neural correlates of this process has significant implications for numerous fields, such as education and health care.
Ana Matran-Fernandez, Sebastian Halder
doaj   +1 more source