Differential entropy feature for EEG-based vigilance estimation
By Li-Chen Shi, Yingying Jiao, Bao-Liang Lu. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013
386 citations recorded by the source corpus.
Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/9153ab92bdac1e49cd0350b8de3a2b5f320202e4
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In Cardiology and Cardiovascular Medicine
In Cognitive Neuroscience
In Experimental and Cognitive Psychology
Citations in the corpus
- Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks (2015, methodology): “After that, we extract efficient features called differential entropy [35], [36] from multichannel EEG data, and then we train deep belief networks with differential entropy features as inputs.”
- Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition (2017, methodology): “proved that differential entropy (DE) was the most accurate and stable feature for emotion recognition than traditional features, including power spectrum density (PSD), autoregressive parameters, fractal dimension, and sample entropy [25, 26].”
- Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, background): “the logarithm energy spectrum in a particular frequency band for a fixed-length EEG sequence [95].”
- EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (2020, methodology): “The commonly used EEG features include the differential entropy (DE) feature [28][29], the power spectral density (PSD) feature [30], the differential asymmetry (DASM) feature [23], the rational asymmetry (RASM) feature [31] and the differential caudality (DCAU) feature [18].”
- EEG Based Emotion Recognition: A Tutorial and Review (2022, background): “Some researches indicate that the computation of DE is equivalent to the logarithm power spectrum in a certain frequency range for a fixed length signal [91].”
- Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition (2021, methodology): “B. Feature extraction 1) EEG feature extraction: For EEG signals, we extract differential entropy (DE) features using short-term Fourier transforms with a 4-second Hanning window without over-lapping [47], [48].”
- Identifying Stable Patterns over Time for Emotion Recognition from EEG (2016, background): “According to [40], in a certain band, DE is equivalent to the logarithmic spectral energy for a fixed-length EEG sequence.”
- Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition (2020, background): “Zheng et al. have proved that differential entropy (DE) [40] was the most accurate and stable feature for emotion recognition than traditional features, including power spectrum density (PSD), autoregressive parameters, fractal dimension, and sample entropy [7], [40], [41].”
- Differential entropy feature for EEG-based emotion classification (2013, background): “It has been proven that, for a fixed length EEG sequence, DE is equivalent to the logarithm ES in a certain frequency band [7].”
- EmotionMeter: A Multimodal Framework for Recognizing Human Emotions (2019, methodology): “After preprocessing the EEG data, we extracted two types of features proposed in our previous studies, namely, power spectral density (PSD) and differential entropy (DE) [55], [66], [67], using short-term Fourier transforms with a 4 s time window without overlapping.”
- EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (2019, background): “The majority of existing features are computed on a single channel, e.g., statistical features [26], PSD [27], differential entropy (DE) [8], and wavelet features [28].”