Spatio-Temporal Filtering for Evoked Potentials Detection
We propose the new application of the spatio-temporal filtering (STF) method, which is a detection of visual evoked potentials applied to brain-computer interfaces (BCI). STF aims in creating a new, enhanced channel basing on the current and the neighbouring samples from all the input channels. The...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9163
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9163
- Palabra clave:
- Brain-computer interfaces
Spatio temporal filtering
Visual evoked potentials
Electrophysiology
Classification accuracy
Generalized eigen decomposition
Input channels
New applications
Spatio temporal filtering
Spatio-temporal filter
Temporal approach
Visual evoked potential
Brain computer interface
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Spatio-Temporal Filtering for Evoked Potentials Detection |
| title |
Spatio-Temporal Filtering for Evoked Potentials Detection |
| spellingShingle |
Spatio-Temporal Filtering for Evoked Potentials Detection Brain-computer interfaces Spatio temporal filtering Visual evoked potentials Electrophysiology Classification accuracy Generalized eigen decomposition Input channels New applications Spatio temporal filtering Spatio-temporal filter Temporal approach Visual evoked potential Brain computer interface |
| title_short |
Spatio-Temporal Filtering for Evoked Potentials Detection |
| title_full |
Spatio-Temporal Filtering for Evoked Potentials Detection |
| title_fullStr |
Spatio-Temporal Filtering for Evoked Potentials Detection |
| title_full_unstemmed |
Spatio-Temporal Filtering for Evoked Potentials Detection |
| title_sort |
Spatio-Temporal Filtering for Evoked Potentials Detection |
| dc.contributor.editor.none.fl_str_mv |
Gruca A. Deorowicz S. Harezlak K. Piotrowska A. Czachorski T. |
| dc.subject.keywords.none.fl_str_mv |
Brain-computer interfaces Spatio temporal filtering Visual evoked potentials Electrophysiology Classification accuracy Generalized eigen decomposition Input channels New applications Spatio temporal filtering Spatio-temporal filter Temporal approach Visual evoked potential Brain computer interface |
| topic |
Brain-computer interfaces Spatio temporal filtering Visual evoked potentials Electrophysiology Classification accuracy Generalized eigen decomposition Input channels New applications Spatio temporal filtering Spatio-temporal filter Temporal approach Visual evoked potential Brain computer interface |
| description |
We propose the new application of the spatio-temporal filtering (STF) method, which is a detection of visual evoked potentials applied to brain-computer interfaces (BCI). STF aims in creating a new, enhanced channel basing on the current and the neighbouring samples from all the input channels. The new channel of the better quality facilitates quick detection of visual evoked potential in the EEG recording by reducing number of averaging operations. The BCI experiments include precise information on the times the specific events took place. This feature allowed us to design very accurately the learning step which is based on generalized eigendecomposition and aims in determining the spatio-temporal filter weights. STF based algorithm allows to achieve good results for enhancement and detection of visual evoked potentials applied for brain-computer interfaces. Advantageous classification accuracies obtained with the use of combined spatial and temporal approach suggest the method can contribute to improvement of the existing solutions and stimulate development of more accurate and faster EEG based interfaces between machines and humans. © 2020, Springer Nature Switzerland AG. |
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2020 |
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2020-03-26T16:33:06Z |
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2020-03-26T16:33:06Z |
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2020 |
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Conferencia |
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Advances in Intelligent Systems and Computing; Vol. 1061, pp. 34-43 |
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9783030319632 |
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21945357 |
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https://hdl.handle.net/20.500.12585/9163 |
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10.1007/978-3-030-31964-9_4 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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57202468264 55985160800 57210822856 |
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Advances in Intelligent Systems and Computing; Vol. 1061, pp. 34-43 9783030319632 21945357 10.1007/978-3-030-31964-9_4 Universidad Tecnológica de Bolívar Repositorio UTB 57202468264 55985160800 57210822856 |
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https://hdl.handle.net/20.500.12585/9163 |
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eng |
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eng |
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2 October 2019 through 3 October 2019 |
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Recurso electrónico |
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application/pdf |
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Springer |
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Springer |
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6th International Conference on Man-Machine Interactions, ICMMI 2019 |
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Gruca A.Deorowicz S.Harezlak K.Piotrowska A.Czachorski T.Piela M.Kotas, MarianOrtiz S.H.C.2020-03-26T16:33:06Z2020-03-26T16:33:06Z2020Advances in Intelligent Systems and Computing; Vol. 1061, pp. 34-43978303031963221945357https://hdl.handle.net/20.500.12585/916310.1007/978-3-030-31964-9_4Universidad Tecnológica de BolívarRepositorio UTB572024682645598516080057210822856We propose the new application of the spatio-temporal filtering (STF) method, which is a detection of visual evoked potentials applied to brain-computer interfaces (BCI). STF aims in creating a new, enhanced channel basing on the current and the neighbouring samples from all the input channels. The new channel of the better quality facilitates quick detection of visual evoked potential in the EEG recording by reducing number of averaging operations. The BCI experiments include precise information on the times the specific events took place. This feature allowed us to design very accurately the learning step which is based on generalized eigendecomposition and aims in determining the spatio-temporal filter weights. STF based algorithm allows to achieve good results for enhancement and detection of visual evoked potentials applied for brain-computer interfaces. Advantageous classification accuracies obtained with the use of combined spatial and temporal approach suggest the method can contribute to improvement of the existing solutions and stimulate development of more accurate and faster EEG based interfaces between machines and humans. © 2020, Springer Nature Switzerland AG.Ministerstwo Nauki i Szkolnictwa Wyższego, MNiSW: BKM-RAu-3/2018This work was partially supported by the Ministry of Science and Higher Education funding for statutory activities of young researchers (BKM-RAu-3/2018).Recurso electrónicoapplication/pdfengSpringerhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075905215&doi=10.1007%2f978-3-030-31964-9_4&partnerID=40&md5=9c60e2a3b0cb717d0df3e7345b03dfad6th International Conference on Man-Machine Interactions, ICMMI 2019Spatio-Temporal Filtering for Evoked Potentials Detectioninfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fBrain-computer interfacesSpatio temporal filteringVisual evoked potentialsElectrophysiologyClassification accuracyGeneralized eigen decompositionInput channelsNew applicationsSpatio temporal filteringSpatio-temporal filterTemporal approachVisual evoked potentialBrain computer interface2 October 2019 through 3 October 2019Akram, F., Han, M., Kim, T., An efficient word typing P300-BCI system using amodified T9 interface and random forest classifier (2015) Comput. Biol. Med., 56, pp. 30-36Blankertz, B., Müller, K., Curio, G., Vaughan, T., Schalk, G., Wolpaw, J., Schlögl, A., Birbaumer, N., The BCI competition 2003: Progress and perspectives in detection and discrimination of EEG single trials (2004) IEEE Trans. Biomed. Eng., 51 (6), pp. 1044-1051Farwell, L.A., Donchin, E., Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials (1988) Electroencephalogr. Clin. Neurophysiol., 70, pp. 510-523Hoffmann, U., Vesin, J., Ebrahimi, T., Diserens, K., An efficient P300-based brain-computer interface for disabled subjects (2008) J. Neurosci. Meth., 167, pp. 115-125Kotas, M., Blaszczyk, J., Moron, T., Spatio-temporal FIR filter for fetal ECG extraction (2015) Int. J. Inf. Electron. Eng. IACSIT Press, 5 (1), pp. 10-14Kotas, M., Jezewski, J., Horoba, K., Matonia, A., Application of spatio-temporal filtering to fetal electrocardiogram enhancement (2011) Comput. Meth. Prog. Biomed., 104, pp. 1-9Kotas, M., Pander, T., Leski, J., Averaging of nonlinearly aligned signal cycles for noise suppression (2015) Biomed. Signal Process. Control, 21, pp. 157-168Krusienski, D., Sellers, E., Cabestaing, F., Bayoudh, S., McFarland, D., Vaughan, T., Wolpaw, J., A comparison of classification techniques for the P300 speller (2006) J. Neural Eng., 3 (4), pp. 299-305Krusienski, D., Sellers, E., McFarland, D., Vaughan, T., Wolpaw, J., Toward enhanced P300 speller performance (2008) J. Neurosci. Meth., 167 (1), pp. 15-21Leski, J., Robust weighted averaging of biomedical signals (2002) IEEE Trans. Biomed. Eng., 49 (8), pp. 796-804Lotte, F., Bougrain, A., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., Yger, F., A review of classification algorithms for EEG-based brain-computer interfaces: A 10-year update (2018) J. Neural Eng., 15 (3)Lotte, F., Congedo, M., Lecuyer, A., Arnaldi, Q.B., A review of classification algorithms for EEG-based brain-computer interfaces (2007) J. Neural Eng., 4, pp. R1-R13Momot, A., Momot, M., Leski, J., Bayesian and empirical bayesian approach to weighted averaging of ECG signal (2007) Tech. Sci., 55 (4)Waytowich, N., Lawhern, V., Bohannon, A., Ball, K., Lance, B., Spectraltransfer learning using information geometry for a user-independent brain-computer interface (2016) Front. Neurosci., 10 (430)Zhang, Y., Zhou, G., Jin, J., Zhao, Q., Wang, X., Cichocki, A., Sparse Bayesian classification of EEG for brain-computer interface (2016) Neural Netw. Learn. Syst., 27 (11), pp. 2256-2267http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9163/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9163oai:repositorio.utb.edu.co:20.500.12585/91632023-04-24 09:39:41.451Repositorio Institucional UTBrepositorioutb@utb.edu.co |