%0 Journal Article
%T Determination of Effective EEG Channels for Discrimination of Positive and Negative Emotions with Wavelet Decomposition and Support Vector Machines
%A Talha Burak ALAKU£¿
%A £¿brahim T¨¹RKO£¿LU
%J -
%D 2019
%X People¡¯s lives and decision-making process are influenced by negative-positive emotions. People state their emotions with words, body language, facial expression and voice during thinking, decision making, observing or interacting with the environment. So, it is vital to understand the nature of emotions well. EEG based emotion recognition systems are useful in brain-computer interface (BCI) area. BCI systems are applied in various fields such as education, healthcare systems, virtual reality, video gaming industry. Although EEG signals give much valuable information about brain functions and emotions, brain-computer interface systems have not attained the targeted goals because of artefacts, misuse of EEG channels, data complexity and inappropriate feature extraction and selection methods. In this article, we tried to analyze which EEG channels are effective to estimate positive-negative emotions. We applied publicly available dataset (DEAP) in this work and 32 different EEG channels were classified. Discrete wavelet decomposition, information measurement and statistical methods were applied in the feature extraction phase. In the last phase, SVM (Support Vector Machines) are applied in order to classify the features. The classification performance of the proposed method evaluated by classification accuracy, log-loss, and ten-fold cross validation. Performance accuracy was observed from each EEG channel and average accuracy was found 74%. The experimental results indicated that the best EEG channels for positive-negative emotions Fp1, FC6, C4, CP1, CP5, CP6, T7, P7, and Pz via the proposed method
%K duygu tahmini
%K destek vekt£¿r makineleri
%K dalgac£¿k d£¿n¨¹£¿¨¹m¨¹
%K entropi
%U http://dergipark.org.tr/gazibtd/issue/47484/482939