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

退縮

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(xué)科排名




書目名稱Brain Informatics網(wǎng)絡(luò)公開度




書目名稱Brain Informatics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Brain Informatics被引頻次




書目名稱Brain Informatics被引頻次學(xué)科排名




書目名稱Brain Informatics年度引用




書目名稱Brain Informatics年度引用學(xué)科排名




書目名稱Brain Informatics讀者反饋




書目名稱Brain Informatics讀者反饋學(xué)科排名

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RAFF

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.

Adherent

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.

Handedness

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.

Urea508

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