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Brain-Computer Interfacing [In the Spotlight

IEEE Signal Processing Magazine, 2010
Recently, CNN reported on the future of brain-computer interfaces (BCIs). BCIs are devices that process a user's brain signals to allow direct communication and interaction with the environment. BCIs bypass the normal neuromuscular output pathways and rely on digital signal processing and machine learning to translate brain signals to action (Figure 1).
Rajesh P. N. Rao, Reinhold Scherer
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Adaptive brain-computer interface

CHI '09 Extended Abstracts on Human Factors in Computing Systems, 2009
Passive brain-computer interfaces are designed to use brain activity as an additional input, allowing the adaptation of the interface in real time according to the user's mental state. While most current brain computer interface research (BCI) is designed for direct use with disabled users, I focus my research on passive BCIs for healthy users.
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Brain-Computer Interfaces for Neurorehabilitation

Critical Reviews in Biomedical Engineering, 2013
Brain-computer interfaces (BCIs) enable control of computers and other assistive devices, such as neuro-prostheses, which are used for communication, movement restoration, neuro-modulation, and muscle stimulation, by using only signals measured directly from the brain. A BCI creates a new output channel for the brain to a computer or a device.
Sujesh, Sreedharan, Ranganatha, Sitaram, Joseph S, Paul, C, Kesavadas   +3 more
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Combination of Augmented Reality Based Brain- Computer Interface and Computer Vision for High-Level Control of a Robotic Arm

IEEE transactions on neural systems and rehabilitation engineering, 2020
Recent advances in robotics, neuroscience, and signal processing make it possible to operate a robot through electroencephalography (EEG)-based brain-computer interface (BCI).
Xiaogang Chen, Xiaoshan Huang, Yijun Wang, Xiaorong Gao   +3 more
semanticscholar   +1 more source

Passive Brain–Computer Interfaces

2018
Passive brain–computer interfaces (passive BCI; pBCI) have been introduced and formally defined almost a decade ago and have gained considerable attention since then. In this chapter, we clarify some points of confusion and provide a perspective on the past, present, and future of the field of passive BCI.
Krol, Laurens R., Andreessen, Lena M., Zander, Thorsten O.   +2 more
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Beamforming in Noninvasive Brain–Computer Interfaces

IEEE Transactions on Biomedical Engineering, 2009
Spatial filtering (SF) constitutes an integral part of building EEG-based brain-computer interfaces (BCIs). Algorithms frequently used for SF, such as common spatial patterns (CSPs) and independent component analysis, require labeled training data for identifying filters that provide information on a subject's intention, which renders these algorithms ...
Moritz Grosse-Wentrup, Christian Liefhold, Klaus Gramann, Martin Buss   +3 more
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Evolutionary Brain Computer Interfaces

2007
We propose a BCI mouse and speller based on the manipulation of P300 waves in EEG signals. The 2---D motion of the pointer on the screen is controlled by directly combining the amplitudes of the output produced by a filter in the presence of different stimuli. This filter and the features to be combined within it are optimised by a GA.
Riccardo Poli, Caterina Cinel, Luca Citi, Francisco Sepulveda   +3 more
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A brain-computer interface for extended reality interfaces

ACM SIGGRAPH 2017 VR Village, 2017
Extended reality (XR) technologies, such as augmented reality (AR) and virtual reality (VR), remain limited in their interaction modalities. Prevailing interaction methods such as hand gestures and voice recognition prove awkward in XR environments, even when performing common tasks (e.g., object selection, menu navigation, and others). In contrast, an
Jay Jantz, Adam Molnar, Ramses Alcaide
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Developing a Novel Tactile P300 Brain-Computer Interface With a Cheeks-Stim Paradigm

IEEE Transactions on Biomedical Engineering, 2020
Objective: Tactile brain-computer interface (BCI) systems can provide new communication and control options for patients with impairments of eye movements or vision. One of the most common modalities used in these BCIs is the P300 potential.
Jing Jin, Zongmei Chen, Ren Xu, Yangyang Miao, Xingyu Wang, T. Jung   +5 more
semanticscholar   +1 more source

The Berlin Brain-Computer Interface

2008
The Berlin Brain-Computer Interface (BBCI) uses a machine learning approach to extract subject-specific patterns from high-dimensional EEG-features optimized for revealing the user’s mental state. Classical BCI application are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([2] and see [3,4,5,6] for ...
Benjamin Blankertz, Michael Tangermann, Florin Popescu, Matthias Krauledat, Siamac Fazli, Márton Dónaczy, Gabriel Curio, Klaus-Robert Müller   +7 more
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