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http://dx.doi.org/10.7471/ikeee.2018.22.3.816

A Study on Training Data Selection Method for EEG Emotion Analysis using Semi-supervised Learning Algorithm  

Yun, Jong-Seob (Dept. of Computer Engineering, Seokyeong University)
Kim, Jin Heon (Dept. of Computer Engineering, Seokyeong University)
Publication Information
Journal of IKEEE / v.22, no.3, 2018 , pp. 816-821 More about this Journal
Abstract
Recently, machine learning algorithms based on artificial neural networks started to be used widely as classifiers in the field of EEG research for emotion analysis and disease diagnosis. When a machine learning model is used to classify EEG data, if training data is composed of only data having similar characteristics, classification performance may be deteriorated when applied to data of another group. In this paper, we propose a method to construct training data set by selecting several groups of data using semi-supervised learning algorithm to improve these problems. We then compared the performance of the two models by training the model with a training data set consisting of data with similar characteristics to the training data set constructed using the proposed method.
Keywords
DEAP; EEG; Emotion Analysis; FFT; Machine Learning;
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