Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text

• Main Authors: Hulliyah, Khodijah, Awang Abu Bakar, Normi Sham
• Format: Article
• Language: English; English
• Published: Turkbilmat Egitim Hizmetleri 2021
• Subjects: T10.5 Communication of technical information
• Online Access: http://irep.iium.edu.my/89662/13/89662_Analysis%20of%20emotion%20recognition%20model%20using%20electroencephalogram.pdf; http://irep.iium.edu.my/89662/19/89662_Analysis%20of%20emotion%20recognition%20model%20using%20electroencephalogram_SCOPUS.pdf; http://irep.iium.edu.my/89662/; https://turcomat.org/index.php/turkbilmat/article/view/910; https://doi.org/10.17762/turcomat.v12i3.910

Recognizing emotions through the brain wave approach with facial or sound expression is widely used, but few use text stimuli. Therefore, this study aims to analyze the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely Support Vector Machine and decision tree as benchmarks. The raw data used comes from the results of scrapping Twitter data. The dataset of emotional annotation was carried out manually based on four classifications, specifically: happiness, sadness, fear, and anger. The annotated dataset was tested using an Electroencephalogram (EEG) device attached to the participant's head to determine the brain waves appearing after reading the text. The results showed that the random forest model has the highest accuracy level with a rate of 98% which is slightly different from the decision tree with 88%. Meanwhile, in SVM the accuracy results are less good with a rate of 32%. Furthermore, the match level of angry emotions from the three models above during manual annotation and using the EEG device showed a high number with an average value above 90%, because reading with angry expressions is easier to perform. For this reason, this study aims to test the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely SVM and decision tree as benchmarks. The dataset used comes from the results of scrapping Twitter data.