{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:27:35Z","timestamp":1774448855703,"version":"3.50.1"},"reference-count":38,"publisher":"Wiley","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["167254"],"award-info":[{"award-number":["167254"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject\u2019s P300 based on his\/her own acquired signals. Our experiments with 21 subjects showed that the SWLDA\u2019s performance using our shape-feature vector was<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mn mathvariant=\"normal\">93<\/mml:mn><mml:mi mathvariant=\"normal\">%<\/mml:mi><\/mml:math>, that is,<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mn mathvariant=\"normal\">10<\/mml:mn><mml:mi mathvariant=\"normal\">%<\/mml:mi><\/mml:math>higher than the one obtained with BCI2000-feature\u2019s vector. The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mrow><mml:mn mathvariant=\"normal\">0.88<\/mml:mn><\/mml:mrow><\/mml:math>. Also, most of the subjects needed less than<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mrow><mml:mn mathvariant=\"normal\">15<\/mml:mn><\/mml:mrow><\/mml:math>trials to have an AUROC superior to<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\"><mml:mrow><mml:mn mathvariant=\"normal\">0.8<\/mml:mn><\/mml:mrow><\/mml:math>. Finally, we found that the electrode C4 also leads to better classification.<\/jats:p>","DOI":"10.1155\/2016\/2029791","type":"journal-article","created":{"date-parts":[[2016,1,10]],"date-time":"2016-01-10T16:01:36Z","timestamp":1452441696000},"page":"1-14","source":"Crossref","is-referenced-by-count":26,"title":["P300 Detection Based on EEG Shape Features"],"prefix":"10.1155","volume":"2016","author":[{"given":"Montserrat","family":"Alvarado-Gonz\u00e1lez","sequence":"first","affiliation":[{"name":"Graduate Program in Computer Science and Engineering, Universidad Nacional Aut\u00f3noma de M\u00e9xico, 04510 Mexico City, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edgar","family":"Gardu\u00f1o","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Instituto de Investigaciones en Matem\u00e1ticas Aplicadas y en Sistemas, Universidad Nacional Aut\u00f3noma de M\u00e9xico, 04510 Mexico City, 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