Convolutional Neural Network for Octave Illusion Classification

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2022/05/01

タイトル(英語): Convolutional Neural Network for Octave Illusion Classification

著者名: Nina Pilyugina (Graduate School of Advanced Science and Technology, Tokyo Denki University), Akihiko Tsukahara (School of Science and Engineering, Tokyo Denki University), Keita Tanaka (Graduate School of Advanced Science and Technology, Tokyo Denki Unive

著者名(英語): Nina Pilyugina (Graduate School of Advanced Science and Technology, Tokyo Denki University), Akihiko Tsukahara (School of Science and Engineering, Tokyo Denki University), Keita Tanaka (Graduate School of Advanced Science and Technology, Tokyo Denki University/School of Science and Engineering, Tokyo Denki University)

キーワード: auditory illusion，octave illusion，convolutional neural network，deep learning，magnetoencephalography

要約(英語): The octave illusion occurs when two tones with one-octave differences are alternately played to both ears repeatedly. This study aims to classify participants into illusion and non-illusion groups by applying a convolutional neural network. Brain activity data were recorded using magnetoencephalography (MEG), and the activation levels between the two groups were analyzed. This study proposes a method for developing several layers of learning units to compare activities in the same brain region for the illusion and non-illusion groups. This study is one of the first attempts to apply deep neural networks for the classification of MEG data to illusion and non-illusion groups. The developed convolutional neural network showed stable results in the classification of octave illusion and non-illusion data with 100% accuracy and low training and validation losses, which indicate that no overfitting occurred. Furthermore, the pre-trained, octave illusion dataset convolutional neural network showed promising results in a similar auditory illusion data classification and can be used as a universal tool for classifying auditory illusions using MEG data.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.142 No.5 （2022） 特集：医用・生体工学関連技術

本誌掲載ページ: 543-549 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/142/5/142_543/_article/-char/ja/