Effects of Data Augmentation Method Borderline-SMOTE on Emotion Recognition of EEG Signals Based on Convolutional Neural Network

In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially electroencephalogram signals, has become a popular research topic and attracted wide attention. However, the imbalance o...

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Veröffentlicht in:	IEEE access Jg. 9; S. 47491 - 47502
Hauptverfasser:	Chen, Yu, Chang, Rui, Guo, Jifeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Arousal Artificial intelligence Artificial neural networks Borderline-Synthetic minority oversampling technique Brain modeling Classification algorithms convolutional neural network Data augmentation Datasets Electroencephalogram Electroencephalography Emotion recognition Emotions Feature extraction Human-computer interface Machine learning Neural networks Oversampling Pattern recognition Physiology Standard data
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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