A brain-controlled switch for asynchronous control applications

By Steven G. Mason, Gary E. Birch. IEEE Transactions on Biomedical Engineering, 2000

326 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/25ebd369aced9becad565eebb51f081ed13d9a41

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In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Brain-computer interfaces for communication and control. (2002, methodology): “Birch and Mason (Birch et al., 1993; Birch and Mason, 2000; Mason and Birch, 2000) describe methods for recognizing potentials related to voluntary movement (VMRPs) in EEG over sensorimotor and supplementary motor cortices, and for using that recognition to control cursor movement.”
An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate (2004, background): “The BCI processes the incoming physiological signals and reacts properly if a known input pattern is found [8], [9].”
A review of classification algorithms for EEG-based brain–computer interfaces (2007, methodology): “Several measures of performance have been proposed in BCI, such as accuracy of classification, Kappa coecient [42], Mutual Information [75], sensitivity and specificity [ 76 ].”
Brain-Computer Interfaces: A Gentle Introduction (2009, background): “In an asynchronous or self-paced BCI, users can interact with a BCI at their leisure, without worrying about well deﬁned time frames [23].”
Brain–computer interface using a simplified functional near-infrared spectroscopy system (2007, background): “the system is active only during defined periods [ 20 ].”
The Hybrid BCI (2010, background): “Second, in both the ERD and SSVEP conditions, some subjects could not attain proficiency, meaning that their classification accuracy was too low for effective communication.”
A general framework for brain-computer interface design (2003, background): “Some BCI Controls in the literature [16], [17] have been designed to recognize when the User is in a passive, observing state as well as in an active control state.”
Brain Computer Interfaces, a Review (2012, methodology): “Continuous wavelet transform (CWT) is defined as the convolution of the signal with the wavelet function , [175]: , , (10), is the wavelet coefficient that corresponds to the frequency associated with the scale s and the time τ of the wavelet function , , and the symbol ‘*’ expresses the complex con”
Linear and nonlinear methods for brain-computer interfaces (2003, methodology): “On the other hand, a nearest neighbor classifier method could be applied in the feature classification process (for example, see [15]).”