Brain-computer interfaces for communication and control.

By Jonathan R. Wolpaw, Niels Birbaumer, Dennis J. McFarland, Gert Pfurtscheller, Theresa M. Vaughan. Clinical Neurophysiology, 2002

5,485 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6de8736706abb37910c5f6769e2f53f1c1855c58

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Brain-Computer Interfaces: Beyond Medical Applications (2012, neutral): “(1) The user is wearing electrodes while running IntendiX.”
Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface (2015, methodology): “To evaluate the performance of the proposed method, classi ﬁ cation accuracy and ITR (Wolpaw et al 2002) were calculated for the online and of ﬂ ine experiments separately.”
Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces? (2006, methodology): “The performance of the SSSEP-based BCI system presented in this work was comparable to existing EEG-based BCIs [3].”
Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. (2005, background): “The main goal of this research work is to establish an EEG-based communication system that should provide an alternative communication or control channel for patients with severe motor impairment [40].”
A review of classification algorithms for EEG-based brain–computer interfaces (2007, background): “A brain–computer interface (BCI) is a communication system that does not require any peripheral muscular activity [1].”
Brain–computer interface using a simplified functional near-infrared spectroscopy system (2007, methodology): “for harnessing such tasks for BCI development [1, 5–8].”
Toward enhanced P300 speller performance. (2008, neutral): “A brain–computer interface (BCI) is a device that uses brain signals to provide a non-muscular communication channel (Wolpaw et al., 2002), particularly for individuals with severe neuromuscular disabilities.”
A P300-based brain-computer interface: initial tests by ALS patients. (2006, methodology): “A BCI allows a user to communicate with devices without voluntary muscle activity (i.e. using only the electrical activity of the brain; Wolpaw et al., 2002).”
Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. (2006, methodology): “Patients select a letter by successively reducing letter strings containing the desired letter by creating SCPs after appearance of the desired letter (Birbaumer et al., 1999; Birbaumer, Hinterberger,Kübler, &Neumann, 2003; Kübler, Kotchoubey, et al., 2001; Perelmouter & Birbaumer, 2000; Tregoubov &”
Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI (2011, background): “Thus, BCIs enable users with severe motor disabilities to use their brain signals for communication and control [1]–[5].”
A brain–computer interface using electrocorticographic signals in humans (2004, methodology): “Brain–computer interfaces (BCIs) convert brain signals into outputs that communicate a user’s intent [1].”
EMG contamination of EEG: spectral and topographical characteristics. (2003, methodology): “The effects of muscle contractions were estimated by R 2 (i.e. the proportion of the variance attributable to contraction level) (Wonnacott and Wonnacott, 1977), calculated with the number of observations equal to the number of trials (Wolpaw et al., 2002).”
Spatio-spectral filters for improving the classification of single trial EEG (2005, background): “[3]),orbymeansofmachine learningwhere given some training examples for each brain state, the task is to”
A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials (2015, methodology): “This study also evaluated the feature values for each method using r -square value (i.e., the proportion of the variance of the signal feature that is accounted for by the user ’ s intent) [1].”
Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification (2008, methodology): “aimed at replacing the impaired functionality of a human neuromuscular system with computer-based analysis of the EEG and establishing a communication channel independent of peripheralnervesandmuscles[ 4 ].MostBCIsutilizeEEGfeaturesthat are well-defined physiologically, which include oscillations in”