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Feature Selection for EEG Data Classification with Weka

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Advances in Swarm Intelligence (ICSI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13345))

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Abstract

The paper explores the application of feature selection techniques for the brain activity classifying patterns task. The study aim is to compare the machine learning algorithms results depending on the chosen feature selection technique. As an example for analysis, the task of classifying of open-eyes and closed-eyes resting states according to EEG data was chosen. For the experiment, EEG records of the resting states from the data set “EEG Motor Movement/Imagery” were used. Features in the time and frequency domains for 19 electrodes corresponding to the 10–20 system were extracted from the EEG records presented in the EDF format. Python was used to form the feature matrix and convert it to ARFF format. The resulting dataset contains 209 features for classification and a target feature. In the experimental part of the work, the classification results are compared before and after feature selection. The experiment examined 10 Weka attribute evaluators.

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Acknowledgment

The research is supported by Ministry of Science and Higher Education of Russian Federation (project No. FSUN-2020–0009).

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Authors and Affiliations

  1. Novosibirsk State Technical University, Karla Marksa Avenue 20, 630073, Novosibirsk, Russia

    Marina Murtazina & Tatiana Avdeenko

Authors
  1. Marina Murtazina
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  2. Tatiana Avdeenko
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Corresponding author

Correspondence to Marina Murtazina .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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Murtazina, M., Avdeenko, T. (2022). Feature Selection for EEG Data Classification with Weka. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13345. Springer, Cham. https://doi.org/10.1007/978-3-031-09726-3_25

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  • DOI: https://doi.org/10.1007/978-3-031-09726-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09725-6

  • Online ISBN: 978-3-031-09726-3

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