{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T01:53:16Z","timestamp":1773539596447,"version":"3.50.1"},"reference-count":26,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T00:00:00Z","timestamp":1588896000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EMORI program"},{"name":"National Agency for the Promotion of Scientific Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2020,5,8]]},"abstract":"<jats:p>Brain-computer interface (BCI) systems translate the human neurophysiological activities into commands through EEG analysis. Improving the BCI performances leads to faster and easier use and less fatigue. In this study, we proposed a new prepossessing approach to increase the robustness of a steady-state visual evoked potential (SSVEP) based BCI. Inspiring from the known properties of the SSVEP frequency components, the goal was to enhance the signal quality by making it more convenient to be interpreted by the decision-making step. We first investigated the potential to detect the deteriorating periods based on the physiological properties of the SSVEP. The proposed system localizes the intervals which can obscure the SSVEP frequencies by a new algorithm founded on the processing and the analysis of the instantaneous phase. The piecewise linear regression allows a sampler comprehension of the phase signal. Then, these intervals are filtered by the moving average filter to enhance the SSVEP quality. Finally, the decision making is made by the canonical correlation analysis (CCA) algorithm. The results of experiments, using real EEG signals from five subjects, show that the proposed approach significantly increases the performances in terms of accuracy and information transfer rate by about 7.3% and 3.85\u2009bits\/min, respectively, in case of 2\u2009s segment length. On the other hand, the spatial filtering methods of the literature weaken the system performances.<\/jats:p>","DOI":"10.1155\/2020\/7459587","type":"journal-article","created":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T19:31:16Z","timestamp":1588966276000},"page":"1-11","source":"Crossref","is-referenced-by-count":5,"title":["SSVEP Enhancement Using Moving Average Filter Controlled by Phase Features"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1253-9970","authenticated-orcid":true,"given":"Omar","family":"Trigui","sequence":"first","affiliation":[{"name":"Advanced Technologies for Medicine and Signals \u201cATMS\u201d, ENIS, Sfax University, Sfax, Tunisia"}]},{"given":"Sawsan","family":"Daoud","sequence":"additional","affiliation":[{"name":"Department of Neurology, Habib Bourguiba University Hospital, Sfax, Tunisia"},{"name":"Neuroscience Laboratory \u201cLR-12-SP-19\u201d, Faculty of Medicine, Sfax University, Sfax, Tunisia"}]},{"given":"Mohamed","family":"Ghorbel","sequence":"additional","affiliation":[{"name":"Advanced Technologies for Medicine and Signals \u201cATMS\u201d, ENIS, Sfax University, Sfax, Tunisia"}]},{"given":"Chokri","family":"Mhiri","sequence":"additional","affiliation":[{"name":"Department of Neurology, Habib Bourguiba University Hospital, Sfax, Tunisia"},{"name":"Neuroscience Laboratory \u201cLR-12-SP-19\u201d, Faculty of Medicine, Sfax University, Sfax, Tunisia"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aaf12e"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/5427154"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aaca6e"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/ab155d"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1007\/s002210100682"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2006.889197"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2015.05.008"},{"key":"8","first-page":"143","volume":"21","year":"1998","journal-title":"Elsevier"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2006.10.019"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1007\/s10548-019-00707-x"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1155\/2007\/94561"},{"issue":"1","key":"12","first-page":"34","volume":"469","year":"2009","journal-title":"Neuroscience Letters"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2006.875576"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2006.889160"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1155\/2009\/864564"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2017.2734164"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2017.08.031"},{"key":"21","year":"1999"},{"key":"23","year":"2011"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.3390\/s18061672"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0077536"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1364-8"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aad567"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jestch.2018.04.002"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1109\/86.712231"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2016.2627556"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/7459587.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/7459587.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2020\/7459587.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T19:31:21Z","timestamp":1588966281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2020\/7459587\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,8]]},"references-count":26,"alternative-id":["7459587","7459587"],"URL":"https:\/\/doi.org\/10.1155\/2020\/7459587","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,8]]}}}