A BCI-based environmental controller for the motion-disabled.

By Xiaorong Gao, Dingfeng Xu, Ming Cheng, Shangkai Gao. IEEE transactions on neural systems and rehabilitation engineering, 2003

576 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/16b6b384f2d547def5da484e1f38865f0dc26495

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, background): “The distinction between VEP and SSVEP depends on the repetition rate of the stimulation (Gao et al 2003b).”
A Brain–Computer Interface Controlled Auditory Event‐Related Potential (P300) Spelling System for Locked‐In Patients (2009, methodology): “In particular, the already existing BCI use steady state–evoked potentials 9 , 10 or the late ERP component P300.”
Brain-computer interfaces in medicine. (2012, methodology): “Steady-state visual evoked potentials have been used for binary selection,(48-51) both discrete and ontinuous control of a cursor in 2 dimensions,(52) prosthesis control,(53) FES,(54,55) spelling,(56) and environmental control.(57) For patients with impaired ision, various auditory(39,40,58-61) and ”
A practical VEP-based brain-computer interface. (2006, methodology): “According to research done in the area of human brain electrophysiology, the amplitude of the SSVEP varies in a complex manner with the frequency of stimulation [12], [14]–[16].”
Brain-Computer Interfaces: A Gentle Introduction (2009, background): “There are a few papers that report BCIs with a high ITR, ranging from 30 bits/min [26, 27] to slightly above 60 bits/min [28] and, most recently, over 90 bits per minute [29].”
Brain-Computer Interfaces Based on Visual Evoked Potentials (2008, methodology): “Using such a stimulator, a 48-target BCI was reported in [8].”
Brain Computer Interfaces, a Review (2012, background): “frequency of visual stimulation is below 6 Hz, while SSVEPs occur in reaction to stimuli of a higher frequency [101,104].”
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs (2006, background): “Among these, the system proposed in [3] attained a high information transfer rate, reaching approximately 60–90 bits/min.”
Visual and Auditory Brain–Computer Interfaces (2014, background): “Flicker / checkerboard SSVEP [10, 11, 15, 89, 90, 91, 92, 93, 94, 95] Sinusoid al carrier tone ASSR [106]”
EEG-Based Brain-Controlled Mobile Robots: A Survey (2013, background): “…which are visually evoked by a stimulus modulated at a ﬁxed frequency and occur as an increase in EEG activity at the stimulus frequency [61], [62]; and 3) the event-related desynchronization (ERD) and event-related synchronization (ERS), which are induced by performing mental tasks, such as…”
A Survey of Stimulation Methods Used in SSVEP-Based BCIs (2010, unknown): “We therefore list the best and median performance of SSVEP-based BCIs using LEDs, checkerboards, and squares here to give an indication: a system using LEDs achieved a bit rate of 68 bits/min with 48 choices [15], a pattern reversal system reached a bit rate of 45.”
Brain-computer interface technology: a review of the Second International Meeting. (2003, methodology): “The work of Gao et al. in Beijing (see [46]) is a very nice demonstration of this method.”
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).”
Towards an independent brain-computer interface using steady state visual evoked potentials. (2008, methodology): “The performance reported in Gao et al. (2003) was from the best subject found during that group's prior work (Cheng et al., 2002), which reported information throughput as low as .76 bits per minute.”
Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects (2009, background): “Effective means for controlling their environments (e.g., controlling room temperature, light, power beds, TV, etc.) would increase their well-being and sense of independence [88], [93], [121].”
Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges (2010, background): “A second level of self-adaptation concerns the choice of the EEG phenomena that each user better controls, which can range from evoked potentials like P300 [46, 138] or SSVEP [182, 58, 22] to spontaneous signals like slow cortical potentials [12] and rhythmic activity [6, 208, 150, 120, 15].”
Brain–computer interface systems: progress and prospects (2007, methodology): “…in which users move a cursor to chosen targets in one [39,48] or more dimensions [15,29,59,81,91] , select one item from two or more choices [60,68] , select items from a scrolling [92] or iterative [66,93] menu, browse the internet [51,61,94,95,205] or navigate a virtual environment…”