Results 91 to 100 of about 28,559 (251)

Multi-command Tactile Brain Computer Interface: A Feasibility Study

, 2013
The study presented explores the extent to which tactile stimuli delivered to the ten digits of a BCI-naive subject can serve as a platform for a brain computer interface (BCI) that could be used in an interactive application such as robotic vehicle ...
Cooper, Eric, Kryssanov, Victor, Makino, Shoji, Matsumoto, Yoshihiro, Mori, Hiromu, Ogawa, Hitoshi, Rutkowski, Tomasz M., Struzik, Zbigniew R.   +7 more
core   +1 more source

Noise reduction in non-invasive brain-computer interfaces for robot control

Current Directions in Biomedical Engineering
To employ a brain-computer interface to control an assistance robot could help disabled people to achieve a minimum degree of autonomy and reduce their dependence on caregivers for simple tasks.
Glaessner Janine, Meyer Dagmar, Schilling Meinhard   +2 more
doaj   +1 more source

The Rise of Human–Computer Integration in Marketing: A Theory Synthesis

Psychology &Marketing, Volume 43, Issue 6, Page 1343-1380, June 2026.
ABSTRACT Human–computer integration (HCInt) technologies, which merge human bodily, cognitive, and sensory functions with computational processes, are reshaping the foundations of consumer experience. Unlike traditional human–computer interaction, HCInt entails adaptive and reciprocal coupling through AI‐driven augmentation, wearables, muscle–computer ...
Carlos Velasco, Kosuke Motoki, Olivia Petit, Aram Nikolai Andersen, Marzieh Alaei, Anders Gustafsson   +5 more
wiley   +1 more source

Explainable AI Insights Into EEG Classification and Its Alignment to Neural Correlates

Human Brain Mapping, Volume 47, Issue 6, April 15, 2026.
We systematically generate, filter, and cluster explanations of deep learning models of EEG data to identify universal patterns of relevance, which we validate via connections to observations from neuroscience. ABSTRACT While deep learning has drastically improved the performance of electroencephalography (EEG) analysis, it remains unclear what these ...
Hendrik Eilts, Gabriel Ivucic, Niklas Koenen, Marvin N. Wright, Tanja Schultz, Felix Putze   +5 more
wiley   +1 more source

Personalized Brain-Computer Interface Models for Motor Rehabilitation

, 2017
We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES).
Grosse-Wentrup, Moritz, Mastakouri, Anastasia-Atalanti, Meyer, Timm, Schölkopf, Bernhard, Weichwald, Sebastian, Özdenizci, Ozan   +5 more
core   +1 more source

Brain Computer Interface Learning for Systems Based on Electrocorticography and Intracortical Microelectrode Arrays

Frontiers in Integrative Neuroscience, 2015
A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively.
Shivayogi V Hiremath, Shivayogi V Hiremath, Weidong eChen, Weidong eChen, Wei eWang, Wei eWang, Wei eWang, Wei eWang, Stephen eFoldes, Stephen eFoldes, Stephen eFoldes, Ying eYang, Ying eYang, Elizabeth Christine Tyler-Kabara, Elizabeth Christine Tyler-Kabara, Elizabeth Christine Tyler-Kabara, Jennifer L Collinger, Jennifer L Collinger, Jennifer L Collinger, Jennifer L Collinger, Michael L Boninger, Michael L Boninger, Michael L Boninger, Michael L Boninger   +23 more
doaj   +1 more source

Advancing Dry Electroencephalography With Scalable, Soft, and Transcranial Magnetic Stimulation‐Compatible Ti3C2Tx MXene Electrodes for Research and Clinical‐Grade Applications

Advanced Science, Volume 13, Issue 21, 13 April 2026.
Dry soft Ti3C2Tx MXene electroencephalography (EEG) electrodes provide low impedance (2.1 ± 1.8 kΩ at 10 Hz), long‐term stability, and enable safe simultaneous EEG and transcranial magnetic stimulation (TMS). Across scalp sites, hair types, and recording paradigms, including steady state visual evoked potentials (SSVEP), clinical EEG, and mobile EEG ...
Sneha Shankar, Jakob Michiels, Ksenija Tasich, Ashley Koluda, Ryan Rich, Brian Erickson, Eugenia Angelopoulos, Francesca Cimino, Daryl Hurwitz, Raghav Garg, Spencer R. Averbeck, Doris Xu, Mariam Josyula, Nina Petillo, James J. Gugger, Kathryn A. Davis, John Medaglia, Flavia Vitale   +17 more
wiley   +1 more source

An uncued brain-computer interface using reservoir computing [PDF]

, 2010
Brain-Computer Interfaces are an important and promising avenue for possible next-generation assistive devices. In this article, we show how Reservoir Comput- ing – a computationally efficient way of training recurrent neural networks – com- bined with a
Buteneers, Pieter, Kindermans, Pieter-Jan, Schrauwen, Benjamin, Verstraeten, David   +3 more
core  

Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface

Frontiers in Neuroinformatics, 2020
A non-invasive, brain-to-brain interface (BBI) requires precision neuromodulation and high temporal resolution as well as portability to increase accessibility. A BBI is a combination of the brain–computer interface (BCI) and the computer–brain interface
John LaRocco, Dong-Guk Paeng
doaj   +1 more source

ControlIt: A Universal Framework for Translational, Adaptive, and Online Brain–Computer Interfaces

Advanced Intelligent Systems, Volume 8, Issue 4, April 2026.
Brain–computer interfaces (BCIs) lack a unified platform that works across signals and algorithms. ControlIt, an open‐source, modular ROS2‐based BCI framework supporting electroencephalography (EEG), electrocorticography (ECoG), and spike‐based decoding across both classification and regression tasks is presented.
Wanlin Yang, Chenyang Li, Xinxiu Xu, Xindong Wang, Yongcheng Guo, Xiaoke Chai, Tianqin Cao, Jiuxiang Song, Nan Wang, Xueming Zhang, Yi Yang, Cong Zheng, He Cui   +12 more
wiley   +1 more source