Open AccessEmotion Classifications in Electroencephalogram (EEG) Signals
Author(s) -
Venothanee Sundra Mohan,
Mohd Fahmi Mohamad Amran,
Yuhanim Hani Yahaya,
Nurhafizah Moziyana Mohd Yusop,
Tengku Mohd Tengku Sembok,
Mohamad Akhtar Ahmad Zainuddin
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b2650.098319
Subject(s) - electroencephalography , adaptability , artifact (error) , feature extraction , artificial intelligence , computer science , arousal , pattern recognition (psychology) , valence (chemistry) , class (philosophy) , feature (linguistics) , signal (programming language) , psychology , speech recognition , social psychology , ecology , linguistics , philosophy , physics , quantum mechanics , psychiatry , biology , programming language
When students are performing bad in their academics or sports activities, there are underlying causes as to why they are unable to concentrate during class and training. This paper describes the method used to obtain, identify and classify emotions from EEG signals captured from students. As the focus on this paper is on military cadets’ performance, the signals are acquired during classes and military training. The acquired signals are pre-processed using artifact removal techniques before sent for feature extraction and finally signals classification based on the valence-arousal emotion model system. The output of the classification will be able to determine if the students are having positive or negative emotions during class thus effecting their concentration level. This paper analyses the current available methods on artifact removals, feature extractions and the training model for the signal classification. Each method is analyzed in accordance to their accuracy, adaptability and the method that results in the least amount of lost data.