Emotion Classifications in Electroencephalogram (EEG) Signals
Venothanee Sundra Mohan1, Mohd Fahmi Mohamad Amran2, Yuhanim Hani Yahaya3, Nurhafizah Moziyana Mohd. Yusop4, Tengku Mohd Tengku Sembok5, Mohamad Akhtar Ahmad Zainuddin6

1Venothanee Sundra Mohan*, Department of Computer Science, National University of Defense Malaysia, Kuala Lumpur, Malaysia.
2Mohd Fahmi Mohamad Amran, Department of Computer Science, National University of Defense Malaysia, Kuala Lumpur, Malaysia.
3Yuhanim Hani Yahaya, Department of Computer Science, National University of Defense Malaysia, Kuala Lumpur, Malaysia.
4Nurhafizah Moziyana Mohd. Yusop, Department of Computer Science, National University of Defense Malaysia, Kuala Lumpur, Malaysia.
5Tengku Mohd Tengku Sembok, Department of Computer Science, National University of Defense Malaysia, Kuala Lumpur, Malaysia.
6Mohamad Akhtar Ahmad Zainuddin, Department of Computer Science, National University of Defense Malaysia, Kuala Lumpur, Malaysia.

Manuscript received on 6 August 2019. | Revised Manuscript received on 13 August 2019. | Manuscript published on 30 September 2019. | PP: 2736-2740 | Volume-8 Issue-3 September 2019 | Retrieval Number: B2650078219/2019©BEIESP | DOI: 10.35940/ijrte.B2650.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Index Terms: Feature extraction; Artifact removal; EEG signal classification; Emotion classification

Scope of the Article: Classification