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After that, the \u201cuniform\u201d LBP operator is carried out on the wavelet-based time\u2013frequency representation. And the generated histogram is regarded as EEG feature vector for the quantification of the textural information of its wavelet coefficients. The LBP features coupled with the support vector machine (SVM) classifier can yield the satisfactory recognition accuracies of 98.88% for interictal and ictal EEG classification and 98.92% for normal, interictal and ictal EEG classification on the publicly available EEG dataset. Moreover, the numerical results on another large size EEG dataset demonstrate that the proposed method can also effectively detect seizure events from multi-channel raw EEG data. Compared with the standard LBP, the \u201cuniform\u201d LBP can obtain the much shorter histogram which greatly reduces the computational burden of classification and enables it to detect ictal EEG signals in real time.<\/jats:p>","DOI":"10.1142\/s0129065718500107","type":"journal-article","created":{"date-parts":[[2018,3,19]],"date-time":"2018-03-19T08:22:40Z","timestamp":1521447760000},"page":"1850010","source":"Crossref","is-referenced-by-count":45,"title":["Epileptic EEG Identification via LBP Operators on Wavelet Coefficients"],"prefix":"10.1142","volume":"28","author":[{"given":"Qi","family":"Yuan","sequence":"first","affiliation":[{"name":"Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250014, P. R. 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