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Emotion recognition based on physiological signals using brain asymmetry index and echo state network

  • S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
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A Correction to this article was published on 15 November 2018

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Abstract

This paper proposes a method to evaluate the degree of emotion being motivated in continuous music videos based on asymmetry index (AsI). By collecting two groups of electroencephalogram (EEG) signals from 6 channels (Fp1, Fp2, Fz and AF3, AF4, Fz) in the left and right hemispheres, multidimensional directed information is used to measure the mutual information shared between two frontal lobes, and then, we get AsI to estimate the degree of emotional induction. In order to evaluate the effect of AsI processing on physiological emotion recognition, 32-channel EEG signals, 2-channel EEG signals and 2-channel EMG signals are selected for each subject from the DEAP dataset, and different sub-bands are extracted using wavelet packet transform. k-means algorithm is used to cluster the wavelet packet coefficients of each sub-band, and the probability distribution of the coefficients under each cluster is calculated. Finally, the probability distribution value of each sample is sent as the original features into echo state network for unsupervised intrinsic plasticity training; the reservoir state nodes are selected as the final feature vector and fed into the support vector machine. The experimental results show that the proposed algorithm can achieve an average recognition rate of 70.5% when the subjects are independent. Compared with the case without AsI, the recognition rate is increased by 8.73%. On the other hand, the ESN is adopted for the original physiological feature refinement which can significantly reduce feature dimensions and be more beneficial to the emotion classification. Therefore, this study can effectively improve the performance of human–machine interface systems based on emotion recognition.

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  • 15 November 2018

    The authors quite agree that the AsI model is first developed by Panagiotis Petrantonakis from references [11] and [18] in their thesis.

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Acknowledgements

This research has been partially supported by National Natural Science Foundation of China (Grant No. 61432004), NSFC-Shenzhen Joint Foundation (Key Project) (Grant No. U1613217).

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Authors and Affiliations

  1. School of Computer and Information, Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, Hefei University of Technology, Hefei, 230601, China

    Fuji Ren, Yindong Dong & Wei Wang

  2. Graduate School of Advanced Technology and Science, University of Tokushima, Tokushima, 7708502, Japan

    Fuji Ren

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  1. Fuji Ren
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Correspondence to Yindong Dong.

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Ren, F., Dong, Y. & Wang, W. Emotion recognition based on physiological signals using brain asymmetry index and echo state network. Neural Comput & Applic 31, 4491–4501 (2019). https://doi.org/10.1007/s00521-018-3664-1

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