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Titlebook: Brain Informatics; International Confer Yi Zeng,Yong He,Qingming Luo Conference proceedings 2017 Springer International Publishing AG 2017

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發(fā)表于 2025-3-21 16:37:36 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Brain Informatics
期刊簡稱International Confer
影響因子2023Yi Zeng,Yong He,Qingming Luo
視頻videohttp://file.papertrans.cn/191/190193/190193.mp4
發(fā)行地址Includes supplementary material:
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Brain Informatics; International Confer Yi Zeng,Yong He,Qingming Luo Conference proceedings 2017 Springer International Publishing AG 2017
影響因子This book constitutes the refereed proceedings of the InternationalConference on Brain Informatics, BI 2017, held in Beijing, China, inNovember 2017.The 31 revised full paperswere carefully reviewed and selected from 64 submissions. BI addresses the computational, cognitive, physiological, biological, physical,ecological and social perspectives of brain informatics, as well as topics related to.mental health and well-being..
Pindex Conference proceedings 2017
The information of publication is updating

書目名稱Brain Informatics影響因子(影響力)




書目名稱Brain Informatics影響因子(影響力)學科排名




書目名稱Brain Informatics網(wǎng)絡公開度




書目名稱Brain Informatics網(wǎng)絡公開度學科排名




書目名稱Brain Informatics被引頻次




書目名稱Brain Informatics被引頻次學科排名




書目名稱Brain Informatics年度引用




書目名稱Brain Informatics年度引用學科排名




書目名稱Brain Informatics讀者反饋




書目名稱Brain Informatics讀者反饋學科排名




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Operating the System, Version 2,g and expression of affective pattern, and then improve the accuracy of recognition. The accuracy of the proposed multi-layer EEG-ER system is compared with various feature extraction methods. For analysis results, average and maximum classification rates of 64% and 67.0% were obtained for arousal and 66.6% and 76.0% for valence.
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發(fā)表于 2025-3-22 15:55:47 | 只看該作者
Learning Music Emotions via Quantum Convolutional Neural Networkms as well as the state-of-the-art in the task of music emotion classification. Moreover, we provide demonstration experiment to explain the good performance of the proposed technique from the perspective of physics and psychology.
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發(fā)表于 2025-3-22 20:49:09 | 只看該作者
EEG-Based Emotion Recognition via Fast and Robust Feature Smoothingesults on the well-known DEAP dataset demonstrate the effectiveness of our approach. Comparing to other studies on the same dataset, ours achieves the shortest feature processing time and the highest classification accuracy on emotion recognition in the valence-arousal quadrant space.
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