CNN and LSTM-Based Emotion Charting Using Physiological Signals

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 20; H. 16; S. 4551
Hauptverfasser:	Dar, Muhammad Najam, Akram, Muhammad Usman, Khawaja, Sajid Gul, Pujari, Amit N.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 14.08.2020 MDPI
Schlagworte:	Algorithms Arousal Classification convolutional neural network (CNN) Datasets ECG EEG Electrocardiography Electroencephalography emotion recognition Emotions Eye movements Galvanic Skin Response GSR Humans long short-term memory (LSTM) Neural networks Neural Networks, Computer Physiology Support vector machines Trends Wavelet transforms convolutional neural network (CNN) physiological signals EEG GSR ECG long short-term memory (LSTM) emotion recognition deep neural network
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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