2022
DOI: 10.3390/diagnostics12102508
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Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals

Abstract: Emotion recognition is one of the most important issues in human–computer interaction (HCI), neuroscience, and psychology fields. It is generally accepted that emotion recognition with neural data such as electroencephalography (EEG) signals, functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS) is better than other emotion detection methods such as speech, mimics, body language, facial expressions, etc., in terms of reliability and accuracy. In particular, EEG signals are bioelec… Show more

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Cited by 15 publications
(3 citation statements)
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“…Frequency components of the EEG are also of high importance. Because oscillations at different frequency levels represent different brain activities [16] . CWT analyzes EEG signals in the time-frequency domain and shows which frequency components are effective in which time interval.…”
Section: Continuous Wavelet Transformmentioning
confidence: 99%
“…Frequency components of the EEG are also of high importance. Because oscillations at different frequency levels represent different brain activities [16] . CWT analyzes EEG signals in the time-frequency domain and shows which frequency components are effective in which time interval.…”
Section: Continuous Wavelet Transformmentioning
confidence: 99%
“…Spectral entropy (SE) is a measure of the complexity of a signal's frequency spectrum [21]. Spectral entropy is a measure of the distribution of power in the frequency domain and does not provide any information about the temporal dynamics of the signal.…”
Section: F Instantaneous Frequency and Spectral Entropymentioning
confidence: 99%
“…Bu süreçte bazı parametre tercihleri (dalgacık tipi, ölçek, sınırlayıcı tipi ve zaman bant genişliği) ile en uygun görüntünün elde edilmesi amaçlanmaktadır. Çünkü daha önceki çalışmalarda [27][28][29][30][31][32][33][34] gösterildiği gibi, ısı haritası görüntü elde etme yöntemlerinde zaman-frekans çözünürlüğü dengesi sonuçlar üzerinde oldukça etkili olmaktadır. Uyku halindeki deneklerden elde edilen BKG sinyalleri, 30 saniyelik parçalara bölünmüş ve toplamda hipertansiyon sınıfından 3000 sinyal parçası normal tansiyondan 3000 sinyal parçası olmak üzere toplamda 6000 sinyal parçası rastgele olarak seçilmiş ve bu sinyal parçaları SDDFB yöntemiyle ısı haritası görüntülerine dönüştürülmüştür.…”
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