Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition

The electroencephalogram (EEG) has great attraction in emotion recognition studies due to its resistance to deceptive actions of humans. This is one of the most significant advantages of brain signals in comparison to visual or speech signals in the emotion recognition context. A major challenge in...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 20; H. 7; S. 2034
Hauptverfasser:	Cimtay, Yucel, Ekmekcioglu, Erhan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 04.04.2020 MDPI
Schlagworte:	Accuracy Classification convolutional neural network Data collection Datasets dense layer EEG Electroencephalography emotion recognition Emotions Entropy Humans Machine Learning Methods Musical performances Neural networks Neural Networks, Computer pretrained models subject independency Support vector machines Wavelet transforms subject independency pretrained models EEG convolutional neural network filtering on output raw data emotion recognition dataset independency dense layer
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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