Emotion Classification from EEG Signals in Convolutional Neural Networks

被引：15

作者：

Donmez, Hayriye ^{[1
]}

Ozkurt, Nalan ^{[1
]}

机构：

[1] Yasar Univ, Dept Elect & Elect Engn, Izmir, Turkey

来源：

2019 INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS CONFERENCE (ASYU) | 2019年

关键词：

EEG; CNN; Deep Learning; Emotion Classification;

D O I：

10.1109/asyu48272.2019.8946364

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The objective of this research is to classify EEG (electroencephalography) signal recordings of the subjects evoked by visual stimulus by using CNN (Convolutional Neural Networks). EEG records the electrical activity of brain signals. In medicine, EEG is used to diagnose some neurological disorders but moreover the classification of the emotions is also possible from EEG recordings. Emotion recognition is an important task for the computers in machine perception. Therefore, in this study the participants are presented with a video containing funny, scary and sad excerpts and simultaneously EEG signal is measured by Neurosky Mindwave EEG Headset. The spectrogram of EEG signals is supplied to CNN and three emotions are classified using brain signal spectrogram images.

引用

页码：104 / 109

页数：6

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