Differential entropy feature for EEG-based vigilance estimation

Shi, Li-Chen; Jiao, Yingying; Lu, Bao-Liang

doi:10.1109/embc.2013.6611075

Cited by 158 publications

(49 citation statements)

References 18 publications

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“…The basic principle of entropy analysis is to extract various entropies from different frequency bands of EEG signals, and these entropies are used to construct the features with the highest discrimination between two types of signals. Previous studies have shown that differential entropy is the most widely used feature in EEG emotion classification ( Shi et al, 2013 ). Differential entropy is the entropy of continuous random variables, which is used to characterize the complexity of continuous random variables and can be defined as…”

Section: Methodsmentioning

confidence: 99%

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder

Duan

et al. 2021

Front. Comput. Neurosci.

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As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.

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Section: Methodsmentioning

confidence: 99%

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder

Duan

et al. 2021

Front. Comput. Neurosci.

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“…The differential entropy characteristic of a certain frequency band is calculated using (1) shown below. Pi can be regarded as an energy spectrum and is equivalent to the value of signal variance multiplying a constant coefficient N ( N is the length of the fixed time window) [37].…”

Section: A Feature Extractionmentioning

confidence: 99%

Dual-Encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation

Tian

Cammon

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep learning methods. To tackle this problem, a dual encoder variational autoencoder-generative adversarial network (DEVAE-GAN) incorporating spatiotemporal features is proposed to generate high-quality artificial samples. First, EEG data for different emotions are preprocessed as differential entropy features under five frequency bands and divided into segments with a 5s time window. Secondly, each feature segment is processed in two forms: the temporal morphology data and the spatial morphology data distributed according to the electrode position. Finally, the proposed dual encoder is trained to extract information from these two features, concatenate the two pieces of information as latent variables, and feed them into the decoder to generate artificial samples. To evaluate the effectiveness, a systematic experimental study was conducted in this work on the SEED dataset. First, the original training dataset is augmented with different numbers of generated samples; then, the augmented training datasets are used to train the deep neural network to construct the sentiment model. The results show that the augmented datasets generated by the proposed method have an average accuracy of 97.21% on all subjects, which is a 5% improvement compared to the original dataset, and the similarity between the generated data and the original data distribution is proved. These results demonstrate that our proposed model can effectively learn the distribution of raw data to generate high-quality artificial samples, which can effectively train a high-precision affective model.

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“…Differential entropy is defined in Eq. 6,where p(x) represents the probability density function of continuous information (Shi et al, 2013).…”

Section: Differential Entropymentioning

confidence: 99%

Like/Dislike Prediction for Sport Shoes With Electroencephalography: An Application of Neuromarketing

Zeng

Lin

Xiao

et al. 2022

Front. Hum. Neurosci.

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Neuromarketing is an emerging research field for prospective businesses on consumer’s preference. Consumer’s preference prediction based on electroencephalography (EEG) can reliably predict likes or dislikes of a product. However, the current EEG prediction and classification accuracy have yet to reach ideal level. In addition, it is still unclear how different brain region information and different features such as power spectral density, brain asymmetry, differential entropy, and Hjorth parameters affect the prediction accuracy. Our study shows that by taking footwear products as an example, the recognition accuracy of product likes or dislikes reaches 94.22%. Compared with other brain regions, the features of the frontal and occipital brain region obtained a higher prediction accuracy, but the fusion of the features of the whole brain region could improve the prediction accuracy of likes or dislikes even further. Future work would be done to correlate the EEG-based like or dislike prediction results with product sales and self-reports.

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Differential entropy feature for EEG-based vigilance estimation

Cited by 158 publications

References 18 publications

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder

Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder

Dual-Encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation

Like/Dislike Prediction for Sport Shoes With Electroencephalography: An Application of Neuromarketing

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