Design and implementation of a brain-computer interface with high transfer rates

By Ming Cheng, Xiaorong Gao, Shangkai Gao, Dingfeng Xu. IEEE Transactions on Biomedical Engineering, 2002

854 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/7eb8a37b5253689b9cff4a7c554a5eff8e2da057

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

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).”
A High-Speed Brain Speller using steady-State Visual evoked potentials (2014, background): “The ON/OFF switch design 40 could be a solution to solve this problem.”
An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method (2009, background): “optimization and channel selection for each subject to improve the performance of BCI have been widely adopted [4, 8, 11].”
Towards an independent brain-computer interface using steady state visual evoked potentials. (2008, methodology): “They can operate in challenging environments with uncontrolled distraction and electrical noise (Cheng et al., 2002; Wang et al., 2004; Lalor et al., 2005; Allison et al., 2006; Trejo et al., 2006) and hence could work well in homes or hospital settings .”
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs (2006, methodology): “The data were recorded in our lab experiments and the hardware settings were described in [10].”
BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI? (2010, background): “One of the most consistent observations in the BCI literature is considerable intersubject variability, and thus the need to customize various parameters according to each user [1], [8], [ 10 ], [11].”
Control of an Electrical Prosthesis With an SSVEP-Based BCI (2008, methodology): “[14] demonstrated a multiclass SSVEPbased BCI to select ten numbers and two control buttons.”
A practical VEP-based brain-computer interface. (2006, background): “exists in the SSVEP-based system [1], [6]–[8].”
The Berlin brain-computer interface: EEG-based communication without subject training (2006, result): “BCI systems relying on evoked potentials can typically achieve higher information transfer rates (ITRs) in contrast to systems working on unstimulated brain signals, see [5].”
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.”
Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis (2013, background): “When subject focuses attention on the repetitive ﬂicker of a visual stimulus, SSVEP is elicited at the same frequency as the ﬂicker frequency and also its harmonics over occipital scalp areas 17 , 18 .”
A BCI-based environmental controller for the motion-disabled. (2003, methodology): “To convert the demonstration systems into practical devices, several problems must be solved [ 11 ].”
Could Anyone Use a BCI? (2010, background): “Other work showed that better displays or other parameters could create more recognizable SSVEPs and similar VEPs (Cheng et al. 2002; Wang et al. 2006; Allison et al. 2008; Bin et al. 2009).”
The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects (2007, background): “BCI systems relying on intentional modulations of evoked potentials can typically achieve higher information transfer rates (ITRs) than systems working on unstimulated brain signals (cf. Cheng et al., 2002; Kaper and Ritter, 2004).”
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.”