2020
DOI: 10.1007/978-981-15-3415-7_34
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Research on EEG Emotional Recognition Based on LSTM

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Cited by 4 publications
(8 citation statements)
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“…b) Frequency domain feature. This is based on the frequency domain of a signal, and several features have been reviewed in previous studies such as power spectral density (PSD) [62], band power using wavelet transform [59], [63], mel-frequency cepstral coefficients (MFCCs) technique [64], and differential entropy (DE) [14], [15], [40], [65], [66]. c) Time-frequency domain feature.…”
Section: Feature Extractionmentioning
confidence: 99%
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“…b) Frequency domain feature. This is based on the frequency domain of a signal, and several features have been reviewed in previous studies such as power spectral density (PSD) [62], band power using wavelet transform [59], [63], mel-frequency cepstral coefficients (MFCCs) technique [64], and differential entropy (DE) [14], [15], [40], [65], [66]. c) Time-frequency domain feature.…”
Section: Feature Extractionmentioning
confidence: 99%
“…The differential entropy (DE) method has, however, been discovered to have the ability to distinguish high energy and low energy patterns from EEG frequencies [14] and also to characterize spatial information from EEG signals [15]. The features generated from the DE method are the most accurate and stable in emotion recognition compared to several others such as autoregressive parameters, fractal dimension, power spectral density (PSD), differential asymmetry (DASM), rational asymmetry (RASM), asymmetry (ASM), differential caudality (DCAU), wavelet features, and sample entropy [40], [65], [66]. The DE formula usually used to characterize an EEG signal is defined as (4) [40].…”
Section: Feature Extractionmentioning
confidence: 99%
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“…In addition to the EEG data oversampling process, there are several vital processes in emotion recognition, such as the feature extraction and the emotion classification processes using the Differential Entropy (DE) method. According to [42], the method is capable of characterizing spatial data from EEG signals with the highlights feature comprising foremost exact and steady features [28,[43][44][45][46]. The classification process in this study compares two methods, namely the Decision Tree and the Convolutional Neural Network.…”
Section: -Literature Reviewmentioning
confidence: 99%
“…Regarding feature extraction, the Differential Entropy (DE) method is used to detect low amplitudes [15], as well as characterize the time series of EEG signals [16]. The most accurate and stable features for emotional recognition are generated by this method [17][18][19]. Although the feature extraction process executed using the DE method tends to consider the characteristics of EEG signals, different participant characteristics significantly affect the emotional responses [12,20].…”
Section: -Introductionmentioning
confidence: 99%