{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:52:23Z","timestamp":1753887143898,"version":"3.41.2"},"reference-count":34,"publisher":"Index Copernicus","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec id=\"j_bams-2020-0013_abs_001_w2aab3b7c72b1b6b1aab1c14b2Aa\">\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>Optimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_bams-2020-0013_abs_002_w2aab3b7c72b1b6b1aab1c14b3Aa\">\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>System of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal\u2019s frequency recognition in offline Brain-Computer Interface (BCI).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_bams-2020-0013_abs_003_w2aab3b7c72b1b6b1aab1c14b4Aa\">\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_bams-2020-0013_abs_004_w2aab3b7c72b1b6b1aab1c14b5Aa\">\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>It is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it\u2019s performance is dependent on subject variability.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1515\/bams-2020-0013","type":"journal-article","created":{"date-parts":[[2020,6,6]],"date-time":"2020-06-06T07:31:28Z","timestamp":1591428688000},"source":"Crossref","is-referenced-by-count":0,"title":["Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm"],"prefix":"10.5604","volume":"16","author":[{"given":"Stanis\u0142aw","family":"Karkosz","sequence":"first","affiliation":[{"name":"SWPS University of Social Sciences and Humanities , Warszawa , Poland"}]},{"given":"Marcin","family":"Jukiewicz","sequence":"additional","affiliation":[{"name":"Adam Mickiewicz University in Pozna\u0144 , Pozna\u0144 , Poland"}]}],"member":"3689","published-online":{"date-parts":[[2020,5,20]]},"reference":[{"key":"2023010916544601504_j_bams-2020-0013_ref_001_w2aab3b7c72b1b6b1ab2b1b1Aa","doi-asserted-by":"crossref","unstructured":"Wolpaw J, Birbaumer N, Heetderks WJ, Mcfarland D, Hunter Peckham P, Schalk G, et al. Brain-Computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng: A Publication IEEE Eng Med Biol Soc 2000;8:164\u201373. https:\/\/doi.org\/10.1109\/TRE.2000.847807.confproc.","DOI":"10.1109\/TRE.2000.847807"},{"key":"2023010916544601504_j_bams-2020-0013_ref_002_w2aab3b7c72b1b6b1ab2b1b2Aa","doi-asserted-by":"crossref","unstructured":"Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948;27:379\u2013423. https:\/\/doi.org\/10.1002\/j.1538-7305.1948.tb01338.x.","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"2023010916544601504_j_bams-2020-0013_ref_003_w2aab3b7c72b1b6b1ab2b1b3Aa","doi-asserted-by":"crossref","unstructured":"Leuthardt EC, Miller KJ, Schalk G, Rao RP, Ojemann JG. Electrocorticography-based brain computer interface-the Seattle experience. IEEE Trans Neural Syst Rehabil Eng 2006;14:194\u20138. https:\/\/doi.org\/10.1109\/TNSRE.2006.875536.","DOI":"10.1109\/TNSRE.2006.875536"},{"key":"2023010916544601504_j_bams-2020-0013_ref_004_w2aab3b7c72b1b6b1ab2b1b4Aa","doi-asserted-by":"crossref","unstructured":"Guger C, Schlogl A, Neuper C, Walterspacher D, Strein T, Pfurtscheller G. Rapid prototyping of an EEG-based brain-computer interface (BCI). IEEE Trans Neural Syst Rehabil Eng 2001;9:49\u201358. https:\/\/doi.org\/10.1109\/7333.918276.","DOI":"10.1109\/7333.918276"},{"key":"2023010916544601504_j_bams-2020-0013_ref_005_w2aab3b7c72b1b6b1ab2b1b5Aa","doi-asserted-by":"crossref","unstructured":"Regan D. A high frequency mechanism which underlies visual evoked potentials. Electroencephalogr Clin Neurophysiol 1968;25:231\u20137. https:\/\/doi.org\/10.1016\/0013-4694(68)90020-5.","DOI":"10.1016\/0013-4694(68)90020-5"},{"key":"2023010916544601504_j_bams-2020-0013_ref_006_w2aab3b7c72b1b6b1ab2b1b6Aa","unstructured":"Oikonomou VP, Liaros G, Georgiadis K, Chatzilari E, Adam K, Nikolopoulos S, et al. Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs. arXiv preprint arXiv:1602.00904; 2016."},{"key":"2023010916544601504_j_bams-2020-0013_ref_007_w2aab3b7c72b1b6b1ab2b1b7Aa","unstructured":"Hakvoort G, Reuderink B, Obbink M. Comparison of PSDA and CCA detection methods in a SSVEP-based BCI-system. Netherlands: Centre for Telematics & Information Technology University of Twente; 2011."},{"key":"2023010916544601504_j_bams-2020-0013_ref_008_w2aab3b7c72b1b6b1ab2b1b8Aa","doi-asserted-by":"crossref","unstructured":"Lin Z, Zhang C, Wu W, Gao X. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng 2006;53:2610\u20134. https:\/\/doi.org\/10.1109\/TBME.2006.886577.","DOI":"10.1109\/TBME.2006.886577"},{"key":"2023010916544601504_j_bams-2020-0013_ref_009_w2aab3b7c72b1b6b1ab2b1b9Aa","doi-asserted-by":"crossref","unstructured":"Nakanishi M, Wang Y, Wang YT, Jung, TP. A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. PloS One 2015;10:e0140703. https:\/\/doi.org\/10.1371\/journal.pone.0140703.","DOI":"10.1371\/journal.pone.0140703"},{"key":"2023010916544601504_j_bams-2020-0013_ref_010_w2aab3b7c72b1b6b1ab2b1c10Aa","doi-asserted-by":"crossref","unstructured":"Bhattacharyya S, Khasnobish A, Chatterjee S, Konar A, Tibarewala DN. Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data. In: 2010 international conference on systems in medicine and biology. IEEE, New Jersey; 2010, 126\u201331 pp.","DOI":"10.1109\/ICSMB.2010.5735358"},{"key":"2023010916544601504_j_bams-2020-0013_ref_011_w2aab3b7c72b1b6b1ab2b1c11Aa","doi-asserted-by":"crossref","unstructured":"Kwak NS, Muller KR, Lee SW. A convolutional neural network for steady state visualevoked potential classification under ambulatory environment. PloS One 2017;12:e0172578. 10.1371\/journal.pone.0172578.","DOI":"10.1371\/journal.pone.0172578"},{"key":"2023010916544601504_j_bams-2020-0013_ref_012_w2aab3b7c72b1b6b1ab2b1c12Aa","doi-asserted-by":"crossref","unstructured":"Lal TN, Schroder M, Hinterberger T, Weston J, Bogdan M, Birbaumer N, Scholkopf B. Support vector channel selection in BCI. IEEE Trans Biomed Eng 2004;51:1003\u201310. https:\/\/doi.org\/10.1109\/TBME.2004.827827.","DOI":"10.1109\/TBME.2004.827827"},{"key":"2023010916544601504_j_bams-2020-0013_ref_013_w2aab3b7c72b1b6b1ab2b1c13Aa","unstructured":"Bakardjian H. Optimization of steady-state visual responses for robust brain-computer interfaces. Tokyo: Department of Electronic and Information Engineering Tokyo University of Agriculture and Technology; 2010."},{"key":"2023010916544601504_j_bams-2020-0013_ref_014_w2aab3b7c72b1b6b1ab2b1c14Aa","doi-asserted-by":"crossref","unstructured":"Martinez P, Bakardjian H, Cichocki A. Fully online multi-command brain-computer interface with visual neurofeedback using SSVEP paradigm. London: Hindawi Publishing Corporation; 2007, vol. 2007.","DOI":"10.1155\/2007\/94561"},{"key":"2023010916544601504_j_bams-2020-0013_ref_015_w2aab3b7c72b1b6b1ab2b1c15Aa","doi-asserted-by":"crossref","unstructured":"Bakardjian H, Tanaka T, Cichocki A. Optimization of SSVEP brain responses with application to eight-command brain\u2013computer interface. Neurosci Lett 2010;469:34\u20138.","DOI":"10.1016\/j.neulet.2009.11.039"},{"key":"2023010916544601504_j_bams-2020-0013_ref_016_w2aab3b7c72b1b6b1ab2b1c16Aa","doi-asserted-by":"crossref","unstructured":"Abootalebi V, Moradi MH, Khalilzadehc MA. A new approach for EEG feature extraction in P300-based lie detection. Comput Methods Progr Biomed 2009;94:48\u201357.","DOI":"10.1016\/j.cmpb.2008.10.001"},{"key":"2023010916544601504_j_bams-2020-0013_ref_017_w2aab3b7c72b1b6b1ab2b1c17Aa","unstructured":"Ko\u0142odziej M. Przetwarzanie, analia i klasyfikacja sygna\u0142u EEG na u\u017cytek interfejsu m\u00f3zg-komputer. Warszawa, Poland: Wydzia\u0142 Elektryczny Uniwersytetu Warszawskiego; 2011."},{"key":"2023010916544601504_j_bams-2020-0013_ref_018_w2aab3b7c72b1b6b1ab2b1c18Aa","unstructured":"Sewell M. Feature selection; 2007. Available from: https:\/\/machine-learning.martinsewell.com\/feature-selection."},{"key":"2023010916544601504_j_bams-2020-0013_ref_019_w2aab3b7c72b1b6b1ab2b1c19Aa","doi-asserted-by":"crossref","unstructured":"del R Millan J, Mouri\u00f1o J, Franz\u00e9 M, Cincotti F, Varsta M, Heikkonen J, Babiloni F. A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Trans Neural Network 2002;13:678\u201386. https:\/\/doi.org\/10.1109\/TNN.2002.1000132.","DOI":"10.1109\/TNN.2002.1000132"},{"key":"2023010916544601504_j_bams-2020-0013_ref_020_w2aab3b7c72b1b6b1ab2b1c20Aa","doi-asserted-by":"crossref","unstructured":"Kantardzic M. Data mining: concepts, models, methods, and algorithms. New Jersey: John Wiley & Sons; 2011.","DOI":"10.1002\/9781118029145"},{"key":"2023010916544601504_j_bams-2020-0013_ref_021_w2aab3b7c72b1b6b1ab2b1c21Aa","doi-asserted-by":"crossref","unstructured":"Welsch RE. Stepwise multiple comparison procedures. J Am Stat Assoc 1977;72:566\u201375.","DOI":"10.1080\/01621459.1977.10480614"},{"key":"2023010916544601504_j_bams-2020-0013_ref_022_w2aab3b7c72b1b6b1ab2b1c22Aa","doi-asserted-by":"crossref","unstructured":"Fisher KA. Statistical tests. Nature 1935;136:474.","DOI":"10.1038\/136474b0"},{"key":"2023010916544601504_j_bams-2020-0013_ref_023_w2aab3b7c72b1b6b1ab2b1c23Aa","unstructured":"Weston J, Mukherjee S, Chapelle O, Pontil M, Poggio T, Vapnik V. Feature selection for SVMs. Advances in neural information processing systems. Massachusetts: Massachusetts Institute of Technology Press; 2001, 668\u201374 pp."},{"key":"2023010916544601504_j_bams-2020-0013_ref_024_w2aab3b7c72b1b6b1ab2b1c24Aa","doi-asserted-by":"crossref","unstructured":"Pudil P, Novovi\u010dov\u00e1 J, Kittler J. Floating search methods in feature selection. Pattern Recogn Lett 1994;15:1119\u201325.","DOI":"10.1016\/0167-8655(94)90127-9"},{"key":"2023010916544601504_j_bams-2020-0013_ref_025_w2aab3b7c72b1b6b1ab2b1c25Aa","unstructured":"Goldberg DE. Genetic algorithms in search. Optimization and machine learning. Addison Wesley Publishing Co. Inc; 1989."},{"key":"2023010916544601504_j_bams-2020-0013_ref_026_w2aab3b7c72b1b6b1ab2b1c26Aa","doi-asserted-by":"crossref","unstructured":"Goldberg DE, Holland JH. Genetic algorithms and machine learning. Mach Learn 1988;3:95\u20139. https:\/\/doi.org\/10.1023\/A:1022602019183.","DOI":"10.1007\/BF00113892"},{"key":"2023010916544601504_j_bams-2020-0013_ref_027_w2aab3b7c72b1b6b1ab2b1c27Aa","unstructured":"Rutkowski, L. Metody i techniki sztucznej inteligencji: inteligencja obliczeniowa. Warsaw: Wydawnictwo Naukowe PWN; 2006."},{"key":"2023010916544601504_j_bams-2020-0013_ref_028_w2aab3b7c72b1b6b1ab2b1c28Aa","doi-asserted-by":"crossref","unstructured":"Flotzinger D, Pregenzer M, Pfurtscheller G. Feature selection with distinction sensitive learning vector quantisation and genetic algorithms. Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN\u201994). IEEE, New Jersey; 1994, 3448\u201351 pp, vol. 6. https:\/\/doi.org\/10.1109\/ICNN.1994.374888.","DOI":"10.1109\/ICNN.1994.374888"},{"key":"2023010916544601504_j_bams-2020-0013_ref_029_w2aab3b7c72b1b6b1ab2b1c29Aa","unstructured":"Sanner MF. Python: a programming language for software integration and development. J Mol Graph Model 1999;17:57\u201361."},{"key":"2023010916544601504_j_bams-2020-0013_ref_030_w2aab3b7c72b1b6b1ab2b1c30Aa","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in python. J Mach Learn Res 2011;12:2825\u201330."},{"key":"2023010916544601504_j_bams-2020-0013_ref_031_w2aab3b7c72b1b6b1ab2b1c31Aa","doi-asserted-by":"crossref","unstructured":"Chen X, Wang Y, Nakanishi M, Gao X, Jung TP, Gao S. High-speed spelling with a noninvasive brain\u2013computer interface. Proc Natl Acad Sci Unit States Am 2015;112:E6058\u201367. https:\/\/doi.org\/10.1073\/pnas.1508080112.","DOI":"10.1073\/pnas.1508080112"},{"key":"2023010916544601504_j_bams-2020-0013_ref_032_w2aab3b7c72b1b6b1ab2b1c32Aa","doi-asserted-by":"crossref","unstructured":"Fatourechi M, Bashashati A, Ward RK, Birch GE. A hybrid genetic algorithm approach for improving the performance of the LF-ASD brain computer interface. In: Proceedings. (ICASSP '05). IEEE international conference on acoustics, speech, and signal processing, 2005. IEEE, New Jersey; 2005, 345\u20138 pp, vol. 5. https:\/\/doi.org\/10.1109\/ICASSP.2005.1416311.","DOI":"10.1109\/ICASSP.2005.1416311"},{"key":"2023010916544601504_j_bams-2020-0013_ref_033_w2aab3b7c72b1b6b1ab2b1c33Aa","unstructured":"Venthur B, Blankertz B. A platform-independent open-source feedback framework for BCI systems. Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course. Verlag der Technischen Universitaet Graz; 2008, 385\u20139 pp."},{"key":"2023010916544601504_j_bams-2020-0013_ref_034_w2aab3b7c72b1b6b1ab2b1c34Aa","doi-asserted-by":"crossref","unstructured":"Akbari H, Khalighinejad B, Herrero JL, Mehta AD, Mesgarani N. Towards reconstructing intelligible speech from the human auditory cortex. Sci Rep 2019;9:874. https:\/\/doi.org\/10.1038\/s41598-018-37359-z.","DOI":"10.1038\/s41598-018-37359-z"}],"container-title":["Bio-Algorithms and Med-Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/view\/journals\/bams\/16\/2\/article-20200013.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/bams-2020-0013\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/bams-2020-0013\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T10:08:00Z","timestamp":1719223680000},"score":1,"resource":{"primary":{"URL":"https:\/\/bamsjournal.com\/resources\/html\/article\/details?id=617693"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,20]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,6,29]]},"published-print":{"date-parts":[[2020,7,22]]}},"alternative-id":["10.1515\/bams-2020-0013"],"URL":"https:\/\/doi.org\/10.1515\/bams-2020-0013","relation":{},"ISSN":["1896-530X","1895-9091"],"issn-type":[{"type":"electronic","value":"1896-530X"},{"type":"print","value":"1895-9091"}],"subject":[],"published":{"date-parts":[[2020,5,20]]},"article-number":"20200013"}}