Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : June 02, 2018 - June 05, 2018
Recently, a lot of researches on image classification methods based on electroencephalogram (EEG) have been reported with good identification rates, closely 80%. However, most of these results were achieved only in silent environment. If the EEG data for evaluation which are measured in noisy environment were applied to the model constructed using other EEG data which are measured in silent environment, the identification rate would be decreased due to the influence of the environment changes. We have ever developed image classification method which is robust to change in the environment using Wavelet analysis and Deep Learning algorithms. In this research, we recorded EEG data at four different places. We evaluate classification method using recorded EEG data. We discuss about image classification method which is more robust to change in the environment.
