Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing.
Background: The signals acquired in brain-computer interface (BCI) experiments usually involve several complicated sampling, artifact and noise conditions. This mandated the use of several strategies as preprocessing to allow the extraction of meaningful components of the measured signals to be pass...
| Publicado en: | BioMedical Engineering OnLine Vol. 13; pp. 36 - 37 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=107843332&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 107843332 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 2014 vid: 13 pid: 24147 pub: BioMed Central artinfo: ui: 107843332 107843332 NLM24708647 2012553762 10.1186/1475-925X-13-36 NLM24708647 PMC3992146 107843332 ppf: 36 ppct: 1 formats: tig: atl: Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing. aug: au: Alhaddad, Mohammed J Kamel, Mahmoud I Makary, Meena M Hargas, Hani Kadah, Yasser M affil: Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. malhaddad@kau.edu.sa. sug: subj: Brain-Computer Interfaces Sensitivity and Specificity Statistics Methods Subtraction Technique Electroencephalography Signal Processing, Computer Assisted ab: Background: The signals acquired in brain-computer interface (BCI) experiments usually involve several complicated sampling, artifact and noise conditions. This mandated the use of several strategies as preprocessing to allow the extraction of meaningful components of the measured signals to be passed along to further processing steps. In spite of the success present preprocessing methods have to improve the reliability of BCI, there is still room for further improvement to boost the performance even more.Methods: A new preprocessing method for denoising P300-based brain-computer interface data that allows better performance with lower number of channels and blocks is presented. The new denoising technique is based on a modified version of the spectral subtraction denoising and works on each temporal signal channel independently thus offering seamless integration with existing preprocessing and allowing low channel counts to be used.Results: The new method is verified using experimental data and compared to the classification results of the same data without denoising and with denoising using present wavelet shrinkage based technique. Enhanced performance in different experiments as quantitatively assessed using classification block accuracy as well as bit rate estimates was confirmed.Conclusion: The new preprocessing method based on spectral subtraction denoising offer superior performance to existing methods and has potential for practical utility as a new standard preprocessing block in BCI signal processing. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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