Improving the Interpretability through Maximizing Mutual Information for EEG Emotion Recognition

IF 9.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Affective Computing Pub Date : 2024-09-18 DOI:10.1109/taffc.2024.3463469

Hua Yang, C. L. Philip Chen, Bianna Chen, Tong Zhang

引用次数: 0

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通过最大化互信息提高脑电图情绪识别的可解释性

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Affective Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS

CiteScore

15.00

自引率

6.20%

发文量

174

期刊介绍： The IEEE Transactions on Affective Computing is an international and interdisciplinary journal. Its primary goal is to share research findings on the development of systems capable of recognizing, interpreting, and simulating human emotions and related affective phenomena. The journal publishes original research on the underlying principles and theories that explain how and why affective factors shape human-technology interactions. It also focuses on how techniques for sensing and simulating affect can enhance our understanding of human emotions and processes. Additionally, the journal explores the design, implementation, and evaluation of systems that prioritize the consideration of affect in their usability. We also welcome surveys of existing work that provide new perspectives on the historical and future directions of this field.