Feature Normalization Effect in Emotion Classification based on EEG Signals

Year 2020, , 60 - 66, 01.02.2020

Orhan Akbulut

https://doi.org/10.16984/saufenbilder.617642

Cited By: 3

Abstract

Normalization of data
in classification-based problem is a fundamental task where binary or multi classifier
systems integrate it as a sub-system.  Normalization
can be thought as a mapping function that makes a transformation from one space
to another space. Different types of normalization methods are proposed
depending on the data content. Recently, researches are carried out on whether
this process is really necessary. In this paper, the performances of the
different normalization methods for Electroencephalogram (EEG) signal based
emotion classification are evaluated. Support vector machine based binary
classifier is used in emotion classification. Different kernel functions for support
vector machine are also considered. Although the experimental findings may not
reveal a significant performance difference between different types of
normalization, the normalization process increases classification performance
of the emotion recognition, in general.

Keywords

normalization, classification, support vector machine

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