@inproceedings{2c46a8b2744b41649ee7da2c1fe178e7,
title = "EEG Signal Discrimination with Permutation Entropy",
abstract = "The information analysis of the electroencephalogram (EEG) signal is carried out by granulation and reciprocal entropy (PeEn). The analysis of the EEG signal is obtained by experimental activity. Due to its complexity and multichannel characteristic, together with granular computing (GrC) and PeEn are used to analyze the EEG signal. The EEG signal consists of 32 channels of data and the experimental data are used to discriminate patterns, with experimental focus on considering real and thinking actions. The time-series EEG signals were granularized according to the changes in the signal and analyzed by PeEn coding and Fuzzy C-Means (FCM) algorithm. Because there are two main actions, i.e., left-handed, and right-handed actions were clearly delineated. In addition, we provide the GrC algorithm to prove the boundary problem with the help of Hilbert-Huang transform. The obtained results show an advanced approach for analyzing EEG signals, which can be the basis for solving complex multichannel data analysis.",
keywords = "Data uncertainty, EEG signal, Granular computing, Granulation, Permutation entropy",
author = "Youpeng Yang and Haolan Zhang and Sanghyuk Lee",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 14th International Conference on Brain Informatics, BI 2021 ; Conference date: 17-09-2021 Through 19-09-2021",
year = "2021",
month = sep,
doi = "10.1007/978-3-030-86993-9_46",
language = "English",
isbn = "9783030869922",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "519--528",
editor = "Mufti Mahmud and Kaiser, {M Shamim} and Stefano Vassanelli and Qionghai Dai and Ning Zhong",
booktitle = "Brain Informatics - 14th International Conference, BI 2021, Proceedings",
}