SanteMusiqueV1, Main, Exploration, bibRecord, 000C88

A MUSIC-based method for SSVEP signal processing.

Identifieur interne : 000C88 ( Main/Exploration ); précédent : 000C87; suivant : 000C89

A MUSIC-based method for SSVEP signal processing.

Auteurs : Kun Chen [République populaire de Chine] ; Quan Liu [République populaire de Chine] ; Qingsong Ai [République populaire de Chine] ; Zude Zhou [République populaire de Chine] ; Sheng Quan Xie [Nouvelle-Zélande] ; Wei Meng [République populaire de Chine]

Source :

Australasian physical & engineering sciences in medicine [ 1879-5447 ] ; 2016.

RBID : pubmed:26831487

Descripteurs français

English descriptors

KwdEn :
- Algorithms (MeSH), Amplifiers, Electronic (MeSH), Computer Simulation (MeSH), Evoked Potentials, Visual (physiology), Humans (MeSH), Photic Stimulation (MeSH), Signal Processing, Computer-Assisted (MeSH), Statistics as Topic (MeSH), Time Factors (MeSH).
MESH :
- physiology : Evoked Potentials, Visual.
- Algorithms, Amplifiers, Electronic, Computer Simulation, Humans, Photic Stimulation, Signal Processing, Computer-Assisted, Statistics as Topic, Time Factors.

Abstract

The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.

DOI: 10.1007/s13246-015-0398-6
PubMed: 26831487

Affiliations:

Nouvelle-Zélande, République populaire de Chine

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000C34
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Le document en format XML

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<term>Humans (MeSH)</term>
<term>Photic Stimulation (MeSH)</term>
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<front><div type="abstract" xml:lang="en">The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.</div>
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<Abstract><AbstractText>The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100%. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications.</AbstractText>
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<affiliations><list><country><li>Nouvelle-Zélande</li>
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<name sortKey="Ai, Qingsong" sort="Ai, Qingsong" uniqKey="Ai Q" first="Qingsong" last="Ai">Qingsong Ai</name>
<name sortKey="Ai, Qingsong" sort="Ai, Qingsong" uniqKey="Ai Q" first="Qingsong" last="Ai">Qingsong Ai</name>
<name sortKey="Chen, Kun" sort="Chen, Kun" uniqKey="Chen K" first="Kun" last="Chen">Kun Chen</name>
<name sortKey="Liu, Quan" sort="Liu, Quan" uniqKey="Liu Q" first="Quan" last="Liu">Quan Liu</name>
<name sortKey="Liu, Quan" sort="Liu, Quan" uniqKey="Liu Q" first="Quan" last="Liu">Quan Liu</name>
<name sortKey="Meng, Wei" sort="Meng, Wei" uniqKey="Meng W" first="Wei" last="Meng">Wei Meng</name>
<name sortKey="Meng, Wei" sort="Meng, Wei" uniqKey="Meng W" first="Wei" last="Meng">Wei Meng</name>
<name sortKey="Zhou, Zude" sort="Zhou, Zude" uniqKey="Zhou Z" first="Zude" last="Zhou">Zude Zhou</name>
<name sortKey="Zhou, Zude" sort="Zhou, Zude" uniqKey="Zhou Z" first="Zude" last="Zhou">Zude Zhou</name>
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<country name="Nouvelle-Zélande"><noRegion><name sortKey="Xie, Sheng Quan" sort="Xie, Sheng Quan" uniqKey="Xie S" first="Sheng Quan" last="Xie">Sheng Quan Xie</name>
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A MUSIC-based method for SSVEP signal processing.

A MUSIC-based method for SSVEP signal processing.

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