Abstract—This paper proposes a method for classifying answers to conversational questions from Electroencephalogram (EEG) data. The proposed method includes steps for EEG recording, feature extraction, and answer classification. For EEG measurements, this paper employs a simple electroencephalograph. The EEG signals from the frontal lobe are recorded. The EEG features are calculated by normalizing the EEG signals and using Convolutional Neural Networks (CNN) for extraction. The answers to questions are then classified from the EEG features using a support vector machine. To show the effectiveness of the proposed method, we conducted experiments using real EEG data. The experimental results confirm that the mean recognition accuracy was 99% or more if the CNN features are individual to the subject. These results suggest that the answers to yes/no questions can be classified using EEG signals and that the EEG analysis technique using CNN and the support vector machine is suitable for extracting and classifying EEG features. 

Index Terms—electroencephalogram, answers of questions, convolutional neural networks, personal differences, human support system, human communication

Cite: Shin-ichi Ito, Momoyo Ito, Shoichiro Fujisawa, and Minoru Fukumi, "Electroencephalogram Data for Classifying Answers to Questions with Neural Networks and Support Vector Machine," International Journal of Signal Processing Systems, Vol. 7, No. 4, pp. 118-122, December 2019. doi: 10.18178/ijsps.7.4.118-122