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A New Method for EEG Signals Classification Based on RBF NN

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Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems (ICETIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 573))

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Abstract

Automation is necessary since traditional EEG assessments are tedious and time-consuming, particularly the outpatient kind. For this manuscript, the researchers focused on constructing a three-class EEG classifier using FeExt and RBFNN, which stands for Radial Basis Functional Neural Network. If FeExt is finished, RBFnn may be trained to equally recognize the trends. Seizure signals are one of the various anomalies that may be identified using the EEG signal. Stable, interactive, and seizure signals are the three different types of EEG signals. This manuscript’s goal is to classify EEG signals using RBFnn. EEG signal data were relied on the CHB-MIT Scalp EEG dataset. There are 55 various FeExt schemes investigated, and a classifier is constructed that is relatively quick and accurate. The 10 morphological features of the literature were not explored or compared with the extraction techniques. According to research, the multilayer perceptron with momentum learning rule is the best classifier topology, and the FeExt algorithms PCA, Bi-gonal 2.2, coif1, DCT, db9, Re-Bi-gonal 1.1, and sym2 perform better than others. The recorded results may be effectively classified for EEG rhythm for quick examination by a neurology professional. Therefore, quick, accurate diagnosis that saves time. Using a similar method, the EEG rhythm categorization for other brain illnesses may be used.

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Authors and Affiliations

  1. College of Computer Science and Information Technology, University of Anbar-Iraq, Anbar, Ramadi, Iraq

    Shokhan M. Al-Barzinji, Mohanad A. Al-Askari & Azmi Shawkat Abdulbaqi

Authors
  1. Shokhan M. Al-Barzinji
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  2. Mohanad A. Al-Askari
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  3. Azmi Shawkat Abdulbaqi
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Corresponding author

Correspondence to Azmi Shawkat Abdulbaqi .

Editor information

Editors and Affiliations

  1. Department of Business Analytics, Sunway University, Subang Jaya, Selangor, Malaysia

    Mohammed A. Al-Sharafi

  2. The British University in Dubai, Dubai, United Arab Emirates

    Mostafa Al-Emran

  3. Al Buraimi University College, Al Buraimi, Oman

    Mohammed Naji Al-Kabi

  4. The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

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Al-Barzinji, S.M., Al-Askari, M.A., Abdulbaqi, A.S. (2023). A New Method for EEG Signals Classification Based on RBF NN. In: Al-Sharafi, M.A., Al-Emran, M., Al-Kabi, M.N., Shaalan, K. (eds) Proceedings of the 2nd International Conference on Emerging Technologies and Intelligent Systems . ICETIS 2022. Lecture Notes in Networks and Systems, vol 573. Springer, Cham. https://doi.org/10.1007/978-3-031-20429-6_7

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