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Literature DB >> 17271308

Lead selection for SSVEP-based brain-computer interface.

Yijun Wang¹, Zhiguang Zhang, Xiaorong Gao, Shangkai Gao.

Abstract

SSVEP-based brain-computer interface (BCI) has potential advantage of high information transfer rate. However, individual difference greatly affects its practical applications. This paper presents a method of lead selection to improve the applicability of SSVEP-based BCI system. Independent component analysis (ICA) is employed to decompose EEGs over visual cortex into SSVEP signal and background noise. Optimal bipolar lead is selected by comparing signal correlation and noise correlation between different channels. The system with one optimal bipolar lead has reached an average transfer rate about 42 bits/min for normal subjects. It has also been successfully applied to an environmental controller for the motion-disabled.

Year: 2004 PMID： 17271308 DOI： 10.1109/IEMBS.2004.1404252

Source DB: PubMed Journal: Conf Proc IEEE Eng Med Biol Soc ISSN： 1557-170X

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Cited

8 in total

1. Towards an independent brain-computer interface using steady state visual evoked potentials.

Authors: Brendan Z Allison; Dennis J McFarland; Gerwin Schalk; Shi Dong Zheng; Melody Moore Jackson; Jonathan R Wolpaw
Journal: Clin Neurophysiol Date: 2008-02 Impact factor: 3.708

2. Exploiting individual primary visual cortex geometry to boost steady state visual evoked potentials.

Authors: M Isabel Vanegas; Annabelle Blangero; Simon P Kelly
Journal: J Neural Eng Date: 2013-04-03 Impact factor: 5.379

3. A multi-command SSVEP-based BCI system based on single flickering frequency half-field steady-state visual stimulation.

Authors: Yunyong Punsawad; Yodchanan Wongsawat
Journal: Med Biol Eng Comput Date: 2016-09-20 Impact factor: 2.602

8. An Efficient Asynchronous High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer Interface speller: The Problem of Individual Differences.

Authors: Saba Ajami; Amin Mahnam; Samane Behtaj; Vahid Abootalebi
Journal: J Med Signals Sens Date: 2018 Oct-Dec

8 in total

Lead selection for SSVEP-based brain-computer interface.

1. Towards an independent brain-computer interface using steady state visual evoked potentials.

2. Exploiting individual primary visual cortex geometry to boost steady state visual evoked potentials.

3. A multi-command SSVEP-based BCI system based on single flickering frequency half-field steady-state visual stimulation.

Review 4. A dynamic selection method for reference electrode in SSVEP-based BCI.

5. A SSVEP Stimuli Encoding Method Using Trinary Frequency-Shift Keying Encoded SSVEP (TFSK-SSVEP).

Review 6. Progress in EEG-Based Brain Robot Interaction Systems.

7. Combining multiple features for error detection and its application in brain-computer interface.

8. An Efficient Asynchronous High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer Interface speller: The Problem of Individual Differences.