Presentation information

General Session

General Session » J-2 Machine learning

[1I5-GS-2] Machine learning: Applied machine learning (2)

Tue. Jun 9, 2020 5:20 PM - 7:00 PM Room I (jsai2020online-9)


5:40 PM - 6:00 PM

[1I5-GS-2-02] Structure Discovery by Visualization of Deep Neural Network for EEG

Approach utilizing Between-Model Variance

〇Kazuki Sakuma1, Junya Morita1, Takatsugu Hirayama2, Yu Enokibori2, Kenji Mase2 (1. Graduate School of Integrated Science and Technology, Department of Engineering, Shizuoka University, 2. Graduate School of Informatics, Nagoya University)

Keywords:DNN, EEG, Visualization

Recently, research using deep learning has been conducted in various fields. However, deep neural network (DNN) has a problem that it is unclear how the model extract features internally. Therefore, "Explainable AI" is required, and research such as visualization has been conducted. In this research, we assume that a learned model with higher classification accuracy pays attention to more essential structure, and compared the visualization results between models which has different classification accuracy. Using this, we examined the method to discover the essential structure of the target phenomenon by the visualization of learned model for the same phenomenon. We conducted an experiment to learn Event Related Potential (ERP) from EEG using DNN. As a result, characteristic structures known in ERP research was obtained from a visualization analysis of the learned model.

Authentication for paper PDF access

A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.