Emotion Classifications in Electroencephalogram (EEG) Signals

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.

A comparative analysis of physiological and functional effectiveness of speech and music perception among musicians and non-musicians

“…Secondary factors are the actual melody of the music, its dynamic, harmonic, rhythmic characteristics, as well as the parameters of the timbre of music (Kaburlasos et al 2019). If music has a tonic effect, then alpha rhythm decreases and the number of fast oscillations and their amplitude increase (Mohan et al 2019). For music intended for relaxation, there is an increase in the alpha rhythm index, its amplitude, but also a parallel decrease in the amplitude of the oscillations (Escoffier, Herrmann, and Schirmer 2015).…”