EEG Based Emotion Prediction with Neural Network Models br

被引：4

作者：

Bardak, F. Kebire ^{[1
]}

Seyman, M. Nuri ^{[1
]}

Temurtas, Feyzullah ^{[1
,2
]}

机构：

[1] Bandirma Onyedi Eylul Univ, Dept Elect & Elect Engn, Bandirma, Balikesir, Turkey

[2] AINTELIA Artificial Intelligence Technol Co, TR-16240 Bursa, Turkey

来源：

TEHNICKI GLASNIK-TECHNICAL JOURNAL | 2022年 / 16卷 / 04期

关键词：

AdaBoost; bagged tree; EEG signals; emotion prediction; multi-layer perceptron; probabilistic neural networks; SVM;

D O I：

10.31803/tg-20220330064309

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The term "emotion" refers to an individual's response to an event, person, or condition. In recent years, there has been an increase in the number of papers that have studied emotion estimation. In this study, a dataset based on three different emotions, utilized to classify feelings using EEG brainwaves, has been analysed. In the dataset, six film clips have been used to elicit positive and negative emotions from a male and a female. However, there has not been a trigger to elicit a neutral mood. Various classification approaches have been used to classify the dataset, including MLP, SVM, PNN, KNN, and decision tree methods. The Bagged Tree technique which is utilized for the first time has been achieved a 98.60 percent success rate in this study, according to the researchers. In addition, the dataset has been classified using the PNN approach, and achieved a success rate of 94.32 percent

引用

页码：497 / 502

页数：6