Abstract
In this paper, we investigate key brain areas of men and women using electroencephalography (EEG) data on recognising three emotions, namely happy, sad and neutral. Considering that emotion changes over time, Long Short-Term Memory (LSTM) neural network is adopted with its capacity of capturing time dependency. Our experimental results indicate that the neural patterns of different emotions have specific key brain areas for males and females, with females showing right lateralization and males being more left lateralized. Accordingly, two non-overlapping brain regions are selected for two genders. The classification accuracy for females (79.14%) using the right lateralized region is significantly higher than that for males (67.61%), and the left lateralized area educes a significantly higher classification accuracy for males (82.54%) than females (73.51%), especially for happy and sad emotions.
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Acknowledgments
This work was supported in part by grants from the National Key Research and Development Program of China (Grant No. 2017YFB1002501), the National Natural Science Foundation of China (Grant No. 61673266), the Major Basic Research Program of Shanghai Science and Technology Committee (Grant No. 15JC1400103), ZBYY-MOE Joint Funding (Grant No. 6141A02022604), and the Technology Research and Development Program of China Railway Corporation (Grant No. 2016Z003-B).
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Yan, X., Zheng, WL., Liu, W., Lu, BL. (2017). Investigating Gender Differences of Brain Areas in Emotion Recognition Using LSTM Neural Network. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_87
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DOI: https://doi.org/10.1007/978-3-319-70093-9_87
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