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 passe...
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Autor*in:	Alhaddad, Mohammed J [verfasserIn] Kamel, Mahmoud I Makary, Meena M Hargas, Hani Kadah, Yasser M

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Brain-computer interface Spectral subtraction Wavelet shrinkage Signal denoising

Anmerkung:	© Alhaddad et al.; licensee BioMed Central Ltd. 2014

Übergeordnetes Werk:	Enthalten in: Biomedical engineering online - London : BioMed Central, 2002, 13(2014), 1 vom: 04. Apr.
Übergeordnetes Werk:	volume:13 ; year:2014 ; number:1 ; day:04 ; month:04

Links:	Volltext

DOI / URN:	10.1186/1475-925X-13-36

Katalog-ID:	SPR028691385

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520			\|a 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.
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allfields	10.1186/1475-925X-13-36 doi (DE-627)SPR028691385 (SPR)1475-925X-13-36-e DE-627 ger DE-627 rakwb eng Alhaddad, Mohammed J verfasserin aut Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Alhaddad et al.; licensee BioMed Central Ltd. 2014 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. Brain-computer interface (dpeaa)DE-He213 Spectral subtraction (dpeaa)DE-He213 Wavelet shrinkage (dpeaa)DE-He213 Signal denoising (dpeaa)DE-He213 Kamel, Mahmoud I aut Makary, Meena M aut Hargas, Hani aut Kadah, Yasser M aut Enthalten in Biomedical engineering online London : BioMed Central, 2002 13(2014), 1 vom: 04. Apr. (DE-627)35210547X (DE-600)2084374-4 1475-925X nnns volume:13 year:2014 number:1 day:04 month:04 https://dx.doi.org/10.1186/1475-925X-13-36 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2014 1 04 04
spelling	10.1186/1475-925X-13-36 doi (DE-627)SPR028691385 (SPR)1475-925X-13-36-e DE-627 ger DE-627 rakwb eng Alhaddad, Mohammed J verfasserin aut Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Alhaddad et al.; licensee BioMed Central Ltd. 2014 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. Brain-computer interface (dpeaa)DE-He213 Spectral subtraction (dpeaa)DE-He213 Wavelet shrinkage (dpeaa)DE-He213 Signal denoising (dpeaa)DE-He213 Kamel, Mahmoud I aut Makary, Meena M aut Hargas, Hani aut Kadah, Yasser M aut Enthalten in Biomedical engineering online London : BioMed Central, 2002 13(2014), 1 vom: 04. Apr. (DE-627)35210547X (DE-600)2084374-4 1475-925X nnns volume:13 year:2014 number:1 day:04 month:04 https://dx.doi.org/10.1186/1475-925X-13-36 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2014 1 04 04
allfields_unstemmed	10.1186/1475-925X-13-36 doi (DE-627)SPR028691385 (SPR)1475-925X-13-36-e DE-627 ger DE-627 rakwb eng Alhaddad, Mohammed J verfasserin aut Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Alhaddad et al.; licensee BioMed Central Ltd. 2014 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. Brain-computer interface (dpeaa)DE-He213 Spectral subtraction (dpeaa)DE-He213 Wavelet shrinkage (dpeaa)DE-He213 Signal denoising (dpeaa)DE-He213 Kamel, Mahmoud I aut Makary, Meena M aut Hargas, Hani aut Kadah, Yasser M aut Enthalten in Biomedical engineering online London : BioMed Central, 2002 13(2014), 1 vom: 04. Apr. (DE-627)35210547X (DE-600)2084374-4 1475-925X nnns volume:13 year:2014 number:1 day:04 month:04 https://dx.doi.org/10.1186/1475-925X-13-36 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2014 1 04 04
allfieldsGer	10.1186/1475-925X-13-36 doi (DE-627)SPR028691385 (SPR)1475-925X-13-36-e DE-627 ger DE-627 rakwb eng Alhaddad, Mohammed J verfasserin aut Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Alhaddad et al.; licensee BioMed Central Ltd. 2014 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. Brain-computer interface (dpeaa)DE-He213 Spectral subtraction (dpeaa)DE-He213 Wavelet shrinkage (dpeaa)DE-He213 Signal denoising (dpeaa)DE-He213 Kamel, Mahmoud I aut Makary, Meena M aut Hargas, Hani aut Kadah, Yasser M aut Enthalten in Biomedical engineering online London : BioMed Central, 2002 13(2014), 1 vom: 04. Apr. (DE-627)35210547X (DE-600)2084374-4 1475-925X nnns volume:13 year:2014 number:1 day:04 month:04 https://dx.doi.org/10.1186/1475-925X-13-36 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2014 1 04 04
allfieldsSound	10.1186/1475-925X-13-36 doi (DE-627)SPR028691385 (SPR)1475-925X-13-36-e DE-627 ger DE-627 rakwb eng Alhaddad, Mohammed J verfasserin aut Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Alhaddad et al.; licensee BioMed Central Ltd. 2014 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. Brain-computer interface (dpeaa)DE-He213 Spectral subtraction (dpeaa)DE-He213 Wavelet shrinkage (dpeaa)DE-He213 Signal denoising (dpeaa)DE-He213 Kamel, Mahmoud I aut Makary, Meena M aut Hargas, Hani aut Kadah, Yasser M aut Enthalten in Biomedical engineering online London : BioMed Central, 2002 13(2014), 1 vom: 04. Apr. (DE-627)35210547X (DE-600)2084374-4 1475-925X nnns volume:13 year:2014 number:1 day:04 month:04 https://dx.doi.org/10.1186/1475-925X-13-36 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2014 1 04 04
language	English
source	Enthalten in Biomedical engineering online 13(2014), 1 vom: 04. Apr. volume:13 year:2014 number:1 day:04 month:04
sourceStr	Enthalten in Biomedical engineering online 13(2014), 1 vom: 04. Apr. volume:13 year:2014 number:1 day:04 month:04
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Brain-computer interface Spectral subtraction Wavelet shrinkage Signal denoising
isfreeaccess_bool	true
container_title	Biomedical engineering online
authorswithroles_txt_mv	Alhaddad, Mohammed J @@aut@@ Kamel, Mahmoud I @@aut@@ Makary, Meena M @@aut@@ Hargas, Hani @@aut@@ Kadah, Yasser M @@aut@@
publishDateDaySort_date	2014-04-04T00:00:00Z
hierarchy_top_id	35210547X
id	SPR028691385
language_de	englisch
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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. 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author	Alhaddad, Mohammed J
spellingShingle	Alhaddad, Mohammed J misc Brain-computer interface misc Spectral subtraction misc Wavelet shrinkage misc Signal denoising Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing
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topic_title	Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing Brain-computer interface (dpeaa)DE-He213 Spectral subtraction (dpeaa)DE-He213 Wavelet shrinkage (dpeaa)DE-He213 Signal denoising (dpeaa)DE-He213
topic	misc Brain-computer interface misc Spectral subtraction misc Wavelet shrinkage misc Signal denoising
topic_unstemmed	misc Brain-computer interface misc Spectral subtraction misc Wavelet shrinkage misc Signal denoising
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title	Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing
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title_full	Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing
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author_browse	Alhaddad, Mohammed J Kamel, Mahmoud I Makary, Meena M Hargas, Hani Kadah, Yasser M
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author-letter	Alhaddad, Mohammed J
doi_str_mv	10.1186/1475-925X-13-36
title_sort	spectral subtraction denoising preprocessing block to improve p300-based brain-computer interfacing
title_auth	Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing
abstract	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. © Alhaddad et al.; licensee BioMed Central Ltd. 2014
abstractGer	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. © Alhaddad et al.; licensee BioMed Central Ltd. 2014
abstract_unstemmed	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. © Alhaddad et al.; licensee BioMed Central Ltd. 2014
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title_short	Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing
url	https://dx.doi.org/10.1186/1475-925X-13-36
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author2	Kamel, Mahmoud I Makary, Meena M Hargas, Hani Kadah, Yasser M
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up_date	2024-07-03T21:06:45.184Z
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Spectral subtraction denoising preprocessing block to improve P300-based brain-computer interfacing

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