Modified Spatio-Temporal Matched Filtering for Brain Responses Classification
In this article, we apply the method of spatio-temporal filtering (STF) to electroencephalographic (EEG) data processing for brain responses classification. The method operates similarly to linear discriminant analysis (LDA) but contrary to most applied classifiers, it uses the whole recorded EEG si...
- Autores:
-
P. Kotas, Marian
Piela, Michal
Contreras-Ortiz, Sonia H.
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/11134
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/11134
- Palabra clave:
- Index Terms—Brain–computer interfaces (BCI)
Discrete cosine transform (DCT)
Generalized matched filtering (GMF)
Spatio– temporal filtering (STF)
Visual evoked potentials (EPs)
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Modified Spatio-Temporal Matched Filtering for Brain Responses Classification |
| title |
Modified Spatio-Temporal Matched Filtering for Brain Responses Classification |
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Modified Spatio-Temporal Matched Filtering for Brain Responses Classification Index Terms—Brain–computer interfaces (BCI) Discrete cosine transform (DCT) Generalized matched filtering (GMF) Spatio– temporal filtering (STF) Visual evoked potentials (EPs) LEMB |
| title_short |
Modified Spatio-Temporal Matched Filtering for Brain Responses Classification |
| title_full |
Modified Spatio-Temporal Matched Filtering for Brain Responses Classification |
| title_fullStr |
Modified Spatio-Temporal Matched Filtering for Brain Responses Classification |
| title_full_unstemmed |
Modified Spatio-Temporal Matched Filtering for Brain Responses Classification |
| title_sort |
Modified Spatio-Temporal Matched Filtering for Brain Responses Classification |
| dc.creator.fl_str_mv |
P. Kotas, Marian Piela, Michal Contreras-Ortiz, Sonia H. |
| dc.contributor.author.none.fl_str_mv |
P. Kotas, Marian Piela, Michal Contreras-Ortiz, Sonia H. |
| dc.subject.keywords.es_CO.fl_str_mv |
Index Terms—Brain–computer interfaces (BCI) Discrete cosine transform (DCT) Generalized matched filtering (GMF) Spatio– temporal filtering (STF) Visual evoked potentials (EPs) |
| topic |
Index Terms—Brain–computer interfaces (BCI) Discrete cosine transform (DCT) Generalized matched filtering (GMF) Spatio– temporal filtering (STF) Visual evoked potentials (EPs) LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
In this article, we apply the method of spatio-temporal filtering (STF) to electroencephalographic (EEG) data processing for brain responses classification. The method operates similarly to linear discriminant analysis (LDA) but contrary to most applied classifiers, it uses the whole recorded EEG signal as a source of information instead of the precisely selected brain responses, only. This way it avoids the limitations of LDA and improves the classification accuracy. We emphasize the significance of the STF learning phase. To preclude the negative influence of super–Gaussian artifacts on accomplishment of this phase, we apply the discrete cosine transform (DCT) based method for their rejection. Later, we estimate the noise covariance matrix using all data available, and we improve the STF template construction. The further modifications are related with the constructed filters operation and consist in the changes of the STF interpretation rules. Consequently, a new tool for evoked potentials (EPs) classification has been developed. Applied to the analysis of signals stored in a publicly available database, prepared for the assessment of modern algorithms aimed in EPs detection (in the frames of the 2019 IFMBE Scientific Challenge), it allowed to achieve the second best result, very close to the best one, and significantly better than the ones achieved by other contestants of the challenge |
| publishDate |
2022 |
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2022-10-27T21:40:54Z |
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2022-10-27T21:40:54Z |
| dc.date.issued.none.fl_str_mv |
2022-08 |
| dc.date.submitted.none.fl_str_mv |
2022-10-26 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/restrictedAccess |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.identifier.citation.es_CO.fl_str_mv |
M. P. Kotas, M. Piela and S. H. Contreras-Ortiz, "Modified Spatio-Temporal Matched Filtering for Brain Responses Classification," in IEEE Transactions on Human-Machine Systems, vol. 52, no. 4, pp. 677-686, Aug. 2022, doi: 10.1109/THMS.2022.3168421 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/11134 |
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10.1109/THMS.2022.3168421 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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M. P. Kotas, M. Piela and S. H. Contreras-Ortiz, "Modified Spatio-Temporal Matched Filtering for Brain Responses Classification," in IEEE Transactions on Human-Machine Systems, vol. 52, no. 4, pp. 677-686, Aug. 2022, doi: 10.1109/THMS.2022.3168421 10.1109/THMS.2022.3168421 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/11134 |
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eng |
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eng |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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openAccess |
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10 Páginas |
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application/pdf |
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Cartagena de Indias |
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IEEE Transactions on Human-Machine Systems - Vol. 52 N° 4 (2022) |
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P. Kotas, Marian02d87d0c-c965-468b-a4a3-21211d1288a7Piela, Michal30612509-13cc-43b9-81ab-31b0b653cab8Contreras-Ortiz, Sonia H.1d56d7f5-97c9-4429-b47d-48ebe97de2a82022-10-27T21:40:54Z2022-10-27T21:40:54Z2022-082022-10-26M. P. Kotas, M. Piela and S. H. Contreras-Ortiz, "Modified Spatio-Temporal Matched Filtering for Brain Responses Classification," in IEEE Transactions on Human-Machine Systems, vol. 52, no. 4, pp. 677-686, Aug. 2022, doi: 10.1109/THMS.2022.3168421https://hdl.handle.net/20.500.12585/1113410.1109/THMS.2022.3168421Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarIn this article, we apply the method of spatio-temporal filtering (STF) to electroencephalographic (EEG) data processing for brain responses classification. The method operates similarly to linear discriminant analysis (LDA) but contrary to most applied classifiers, it uses the whole recorded EEG signal as a source of information instead of the precisely selected brain responses, only. This way it avoids the limitations of LDA and improves the classification accuracy. We emphasize the significance of the STF learning phase. To preclude the negative influence of super–Gaussian artifacts on accomplishment of this phase, we apply the discrete cosine transform (DCT) based method for their rejection. Later, we estimate the noise covariance matrix using all data available, and we improve the STF template construction. The further modifications are related with the constructed filters operation and consist in the changes of the STF interpretation rules. Consequently, a new tool for evoked potentials (EPs) classification has been developed. Applied to the analysis of signals stored in a publicly available database, prepared for the assessment of modern algorithms aimed in EPs detection (in the frames of the 2019 IFMBE Scientific Challenge), it allowed to achieve the second best result, very close to the best one, and significantly better than the ones achieved by other contestants of the challenge10 Páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2IEEE Transactions on Human-Machine Systems - Vol. 52 N° 4 (2022)Modified Spatio-Temporal Matched Filtering for Brain Responses Classificationinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Index Terms—Brain–computer interfaces (BCI)Discrete cosine transform (DCT)Generalized matched filtering (GMF)Spatio– temporal filtering (STF)Visual evoked potentials (EPs)LEMBCartagena de IndiasL. 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