Differential entropy feature for EEG-based emotion classification

By Ruo-Nan Duan, Jia-Yi Zhu, Bao-Liang Lu. International IEEE/EMBS Conference on Neural Engineering, 2013

1,075 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/8e4186fe7ee260ebdb5bcf75d3ae75073d64c899

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Wei-Long Zheng
2. Bao-Liang Lu
3. Wei Liu
4. Dongrui Wu
5. Zhen Cui
6. Wenming Zheng
7. Jinpeng Li
8. Huiguang He

In Experimental and Cognitive Psychology

1. Wei-Long Zheng
2. Bao-Liang Lu
3. Wei Liu
4. Dongrui Wu
5. Zhen Cui
6. Wenming Zheng
7. Jinpeng Li
8. Huiguang He

Citations in the corpus

• 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].”
• 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.”
• Identifying Stable Patterns over Time for Emotion Recognition from EEG (2016, methodology): “We compute differential asymmetry (DASM) and rational asymmetry (RASM) features as the differences and ratios between the DE features of 27 pairs of hemispheric asymmetry electrodes (Fp1-Fp2, F7-F8, F3-F4, FT7-FT8, FC3-FC4, T7-T8, P7-P8, C3-C4, TP7-TP8, CP3-CP4, P3-P4, O1-O2, AF3-AF4, F5-F6, F7F8, F”
• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “2 SEED [97,98] 2015 ■ / / / / / / 15 Positive, Neutral, and Negative”
• Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks (2015, methodology): “An efficient feature called differential entropy (DE) [35], [36] extends the idea of Shannon entropy and is used to measure the complexity of a continuous random variable [46].”
• Targeting EEG/LFP Synchrony with Neural Nets (2017, background): “Often this requires calculating synchrony-or entropy-based features within predefined frequency bands, such as [19, 5, 9 , 13].”
• 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].”
• Spatial–Temporal Recurrent Neural Network for Emotion Recognition (2017, methodology): “and differential entropy (DE) [7] are employed, and popular classifiers, such as support vector machine (SVM) [8] and group sparse CCA (GSCCA) [11] are used to achieve classifi-”
• EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention (2023, methodology): “to the literatures [18] and [51], the band-pass filter is applied to EEG signals to obtain the sub-band signals, which approximately follow a Gaussian distribution.”
• A Survey on Negative Transfer (2020, methodology): “To reduce the computational cost, we first extracted 40 common classes used in [180], and selected three domains: Clipart (C), Infograph (I), and Painting (P).”
• 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].”
• EEG-based emotion classification using deep belief networks (2014, background): “It has been proven that, for a fixed length EEG segment, differential entropy is equivalent to the logarithm energy spectrum in a certain frequency band [5].”