Brain-computer interfaces based on the steady-state visual-evoked response.

By Matthew Middendorf, G. McMillan, G. Calhoun, Keith S. Jones. IEEE transactions on rehabilitation engineering, 2000

711 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d76beb59a23c01c9bec1940c4cec1ca26e00480a

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In Cognitive Neuroscience

Citations in the corpus

The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, methodology): “Two dry sensor systems have been evaluated with respect to an SSVEP (Morgan et al., 1996; Middendorf et al., 2000; Cheng et al., 2002; Allison et al., 2008) paradigm: Luo and Sullivan (2010) used a single-channel dry sensor in a four-class setting.”
A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation (2009, background): “One example is the wheelchair developed by Gr¨ aser et al. [13], which uses steady-state potentials [ 14 ].”
Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces (2007, methodology): “The bipolar technique has been employed previously in [5], [10] for brain-computer interfacing based on SSVEP.”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, background): “Thus, for example, a system that uses visual evoked potentials (VEPs) to detect gaze direction [24], [25] is a dependent BCI because it requires neuromuscular control of eye (or head) movements.”
A BCI-based environmental controller for the motion-disabled. (2003, methodology): “Middendorf presented two buttons modulated at 17.56 and 23.42 Hz to induce multiple SSVEPs [ 10 ].”
Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface (2015, methodology): “This study designed a 40-target BCI speller using a frequency coding approach (Middendorf et al 2000, Cheng et al 2002, Wang et al 2006).”
Brain-computer communication: unlocking the locked in. (2001, methodology): “The brain-computer interfaces of, for example, Farwell and Donchin (1988); Donchin, Spencer, and Wijesinghe (2000); Bayliss and Ballard (2000); Sutler (1992); and Middendorf et al. (2000) use event-related potentials as the target signal.”
Control of an Electrical Prosthesis With an SSVEP-Based BCI (2008, methodology): “Middendorf and colleagues [12], [ 13 ] used such a BCI for the control of a flight simulator and the selection of one out of two buttons.”
Brain-computer interface technology: a review of the first international meeting. (2000, background): “With the exception of systems that depend on muscle control [2], [10], [25], and the possible exception of the P300 BCI [4], current BCI’s depend on EEG or single-unit control that is acquired through operant conditioning procedures.”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “Middendorf et al. (2000) designed a simple ﬂight simulator that is controlled by a BCI based on Steady-State Visual-Evoked Response (SSVER).”
Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components (2005, methodology): “Recently proposed SSVEP-based BCIs were implemented on the basis of the ﬁrst or ﬁrst and second harmonics (Middendorf et al 2000, Gao et al 2003).”
EEG feature extraction based on wavelet packet decomposition for brain computer interface (2008, background): “There are 64 sub-bands of wavelet packet at the 6th level whose corresponding frequency ranges are [0,2], [2,4], .”
Brain–computer interface systems: progress and prospects (2007, background): “…[101,102] , common appliances, such as an air conditioner, television or music player [66,68,130] , medical devices [6,61,66,68,93,103] , robotic arms [80,81,84] , mobile robots [77] , functional electrical stimulators or orthoses [18,24,44,45,67,104] and a full-motion flight simulator [67] .”
The brain–computer interface cycle (2009, neutral): “BCI [47, 48].”