Literature DB >> 16792302

A practical VEP-based brain-computer interface.

Yijun Wang1, Ruiping Wang, Xiaorong Gao, Bo Hong, Shangkai Gao.   

Abstract

This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to > 90% of people with a high ITR in living environments.

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Year:  2006        PMID: 16792302     DOI: 10.1109/TNSRE.2006.875576

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  57 in total

Review 1.  Brain computer interfaces, a review.

Authors:  Luis Fernando Nicolas-Alonso; Jaime Gomez-Gil
Journal:  Sensors (Basel)       Date:  2012-01-31       Impact factor: 3.576

2.  High-speed spelling with a noninvasive brain-computer interface.

Authors:  Xiaogang Chen; Yijun Wang; Masaki Nakanishi; Xiaorong Gao; Tzyy-Ping Jung; Shangkai Gao
Journal:  Proc Natl Acad Sci U S A       Date:  2015-10-19       Impact factor: 11.205

3.  Describing different brain computer interface systems through a unique model: a UML implementation.

Authors:  Lucia Rita Quitadamo; Maria Grazia Marciani; Gian Carlo Cardarilli; Luigi Bianchi
Journal:  Neuroinformatics       Date:  2008-07-08

4.  Electroencephalography (EEG)-based neurofeedback training for brain-computer interface (BCI).

Authors:  Kyuwan Choi
Journal:  Exp Brain Res       Date:  2013-09-26       Impact factor: 1.972

5.  Control of a vehicle with EEG signals in real-time and system evaluation.

Authors:  Kyuwan Choi
Journal:  Eur J Appl Physiol       Date:  2011-06-11       Impact factor: 3.078

6.  Identification of task parameters from movement-related cortical potentials.

Authors:  Ying Gu; Omar Feix do Nascimento; Marie-Françoise Lucas; Dario Farina
Journal:  Med Biol Eng Comput       Date:  2009-12       Impact factor: 2.602

7.  EEG-based hybrid QWERTY mental speller with high information transfer rate.

Authors:  Er Akshay Katyal; Rajesh Singla
Journal:  Med Biol Eng Comput       Date:  2021-02-16       Impact factor: 2.602

Review 8.  Physiological properties of brain-machine interface input signals.

Authors:  Marc W Slutzky; Robert D Flint
Journal:  J Neurophysiol       Date:  2017-06-14       Impact factor: 2.714

9.  Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis.

Authors:  Masaki Nakanishi; Yijun Wang; Xiaogang Chen; Yu-Te Wang; Xiaorong Gao; Tzyy-Ping Jung
Journal:  IEEE Trans Biomed Eng       Date:  2017-04-19       Impact factor: 4.538

Review 10.  A survey of stimulation methods used in SSVEP-based BCIs.

Authors:  Danhua Zhu; Jordi Bieger; Gary Garcia Molina; Ronald M Aarts
Journal:  Comput Intell Neurosci       Date:  2010-03-07
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