Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis o...
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Autor*in:	Smolinski, Tomasz G [verfasserIn] Buchanan, Roger Boratyn, Grzegorz M Milanova, Mariofanna Prinz, Astrid A

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2006

Schlagwörter:	Basis Function Mean Square Error Independent Component Analysis Reconstruction Error Generalization Error

Anmerkung:	© Smolinski et al; licensee BioMed Central Ltd. 2006

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 7(2006), Suppl 2 vom: 26. Sept.
Übergeordnetes Werk:	volume:7 ; year:2006 ; number:Suppl 2 ; day:26 ; month:09

Links:	Volltext

DOI / URN:	10.1186/1471-2105-7-S2-S8

Katalog-ID:	SPR026834006

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520			\|a Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself.
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spelling	10.1186/1471-2105-7-S2-S8 doi (DE-627)SPR026834006 (SPR)1471-2105-7-S2-S8-e DE-627 ger DE-627 rakwb eng Smolinski, Tomasz G verfasserin aut Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Smolinski et al; licensee BioMed Central Ltd. 2006 Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself. Basis Function (dpeaa)DE-He213 Mean Square Error (dpeaa)DE-He213 Independent Component Analysis (dpeaa)DE-He213 Reconstruction Error (dpeaa)DE-He213 Generalization Error (dpeaa)DE-He213 Buchanan, Roger aut Boratyn, Grzegorz M aut Milanova, Mariofanna aut Prinz, Astrid A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 7(2006), Suppl 2 vom: 26. Sept. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:7 year:2006 number:Suppl 2 day:26 month:09 https://dx.doi.org/10.1186/1471-2105-7-S2-S8 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_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_2111 GBV_ILN_2113 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2006 Suppl 2 26 09
allfields_unstemmed	10.1186/1471-2105-7-S2-S8 doi (DE-627)SPR026834006 (SPR)1471-2105-7-S2-S8-e DE-627 ger DE-627 rakwb eng Smolinski, Tomasz G verfasserin aut Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Smolinski et al; licensee BioMed Central Ltd. 2006 Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself. Basis Function (dpeaa)DE-He213 Mean Square Error (dpeaa)DE-He213 Independent Component Analysis (dpeaa)DE-He213 Reconstruction Error (dpeaa)DE-He213 Generalization Error (dpeaa)DE-He213 Buchanan, Roger aut Boratyn, Grzegorz M aut Milanova, Mariofanna aut Prinz, Astrid A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 7(2006), Suppl 2 vom: 26. Sept. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:7 year:2006 number:Suppl 2 day:26 month:09 https://dx.doi.org/10.1186/1471-2105-7-S2-S8 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_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_2111 GBV_ILN_2113 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2006 Suppl 2 26 09
allfieldsGer	10.1186/1471-2105-7-S2-S8 doi (DE-627)SPR026834006 (SPR)1471-2105-7-S2-S8-e DE-627 ger DE-627 rakwb eng Smolinski, Tomasz G verfasserin aut Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Smolinski et al; licensee BioMed Central Ltd. 2006 Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself. Basis Function (dpeaa)DE-He213 Mean Square Error (dpeaa)DE-He213 Independent Component Analysis (dpeaa)DE-He213 Reconstruction Error (dpeaa)DE-He213 Generalization Error (dpeaa)DE-He213 Buchanan, Roger aut Boratyn, Grzegorz M aut Milanova, Mariofanna aut Prinz, Astrid A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 7(2006), Suppl 2 vom: 26. Sept. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:7 year:2006 number:Suppl 2 day:26 month:09 https://dx.doi.org/10.1186/1471-2105-7-S2-S8 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_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_2111 GBV_ILN_2113 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2006 Suppl 2 26 09
allfieldsSound	10.1186/1471-2105-7-S2-S8 doi (DE-627)SPR026834006 (SPR)1471-2105-7-S2-S8-e DE-627 ger DE-627 rakwb eng Smolinski, Tomasz G verfasserin aut Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Smolinski et al; licensee BioMed Central Ltd. 2006 Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself. Basis Function (dpeaa)DE-He213 Mean Square Error (dpeaa)DE-He213 Independent Component Analysis (dpeaa)DE-He213 Reconstruction Error (dpeaa)DE-He213 Generalization Error (dpeaa)DE-He213 Buchanan, Roger aut Boratyn, Grzegorz M aut Milanova, Mariofanna aut Prinz, Astrid A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 7(2006), Suppl 2 vom: 26. Sept. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:7 year:2006 number:Suppl 2 day:26 month:09 https://dx.doi.org/10.1186/1471-2105-7-S2-S8 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_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_2111 GBV_ILN_2113 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2006 Suppl 2 26 09
language	English
source	Enthalten in BMC bioinformatics 7(2006), Suppl 2 vom: 26. Sept. volume:7 year:2006 number:Suppl 2 day:26 month:09
sourceStr	Enthalten in BMC bioinformatics 7(2006), Suppl 2 vom: 26. Sept. volume:7 year:2006 number:Suppl 2 day:26 month:09
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Basis Function Mean Square Error Independent Component Analysis Reconstruction Error Generalization Error
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	Smolinski, Tomasz G @@aut@@ Buchanan, Roger @@aut@@ Boratyn, Grzegorz M @@aut@@ Milanova, Mariofanna @@aut@@ Prinz, Astrid A @@aut@@
publishDateDaySort_date	2006-09-26T00:00:00Z
hierarchy_top_id	326644814
id	SPR026834006
language_de	englisch
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author	Smolinski, Tomasz G
spellingShingle	Smolinski, Tomasz G misc Basis Function misc Mean Square Error misc Independent Component Analysis misc Reconstruction Error misc Generalization Error Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials
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topic_title	Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials Basis Function (dpeaa)DE-He213 Mean Square Error (dpeaa)DE-He213 Independent Component Analysis (dpeaa)DE-He213 Reconstruction Error (dpeaa)DE-He213 Generalization Error (dpeaa)DE-He213
topic	misc Basis Function misc Mean Square Error misc Independent Component Analysis misc Reconstruction Error misc Generalization Error
topic_unstemmed	misc Basis Function misc Mean Square Error misc Independent Component Analysis misc Reconstruction Error misc Generalization Error
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title	Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials
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title_full	Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials
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author_browse	Smolinski, Tomasz G Buchanan, Roger Boratyn, Grzegorz M Milanova, Mariofanna Prinz, Astrid A
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title_sort	independent component analysis-motivated approach to classificatory decomposition of cortical evoked potentials
title_auth	Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials
abstract	Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself. © Smolinski et al; licensee BioMed Central Ltd. 2006
abstractGer	Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself. © Smolinski et al; licensee BioMed Central Ltd. 2006
abstract_unstemmed	Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself. © Smolinski et al; licensee BioMed Central Ltd. 2006
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title_short	Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials
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up_date	2024-07-03T22:59:23.374Z
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Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

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