Investigation on identifying implicit learning event from EEG signal using multiscale entropy and artificial bee colony

Chayapol Chaiyanan, Keiji Iramina, Boonserm Kaewkamnerdpong

研究成果: ジャーナルへの寄稿学術誌査読

1 被引用数 (Scopus)

抄録

The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type of learning of the underlying rules without consciously seeking or understanding the rules; it is commonly seen in small children while learning how to speak their native language without learning grammar. This research aims to introduce a processing system that can systematically identify the relationship between implicit learning events and their Encephalogram (EEG) signal characteristics. This study converted the EEG signal from participants while performing cognitive task experiments into Multiscale Entropy (MSE) data. Using MSE data from different frequency bands and channels as features, the system explored a wide range of classifiers and observed their performance to see how they classified the features related to participants’ performance. The Artificial Bee Colony (ABC) method was used for feature selection to improve the process to make the system more efficient. The results showed that the system could correctly identify the differences between participants’ performance using MSE data and the ABC method with 95% confidence.

本文言語英語
論文番号617
ジャーナルEntropy
23
5
DOI
出版ステータス出版済み - 5月 2021

!!!All Science Journal Classification (ASJC) codes

  • 情報システム
  • 数理物理学
  • 物理学および天文学(その他)
  • 電子工学および電気工学

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