PSPN:伪罗金字塔网络用于多式联络情绪分析.
Yanyan Yin1,2, Wanzeng Kong1,2, Jiajia Tang1,2
1College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018 China.
Cognitive neurodynamics
|November 18, 2024
概括
这项研究引入了伪罗金字塔网络 (PSPN) 用于情感识别,结合了脑电图 (EEG) 和与眼睛相关的信号. 多模式方法显著提高了情绪识别的准确性,优于单模式方法.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 情绪识别对于医疗保健和日常生活至关重要.
- 电脑电图 (EEG) 是强烈情绪的关键客观指标.
- 目前的方法在复杂情绪方面扎在浅/深EEG特征和单调局限性上.
研究的目的:
- 提出一个新的多式联络情绪分析框架,整合EEG和与眼睛相关的信号.
- 通过结合补充数据源来解决当前情绪识别方法的局限性.
- 为了提高情绪识别的准确性和全面性.
主要方法:
- 开发了一个伪罗金字塔网络 (PSPN) 用于多式情绪分析.
- 使用深度可分离的卷积金字塔 (DSCP) 进行多层次的EEG特征提取.
- 采用完全连接的子网络用于与眼睛相关的信号特征提取.
- 集成了一个跨模式双分支子网络,用于协作功能利用.
主要成果:
- 在公共数据集 (DEAP和SEED-IV) 上,PSPN模型实现了高精度.
- 在DEAP数据集上,达到了96.02%的价值准确度和96.45%的兴奋准确度.
- 在SEED-IV数据集上达到77.81%的准确性.
- 通过多式联接与单式联接方法相比,通过多式联接方法显著提高了准确性.
结论:
- 使用EEG和与眼睛相关的信号的多式融合显著增强了情绪识别.
- 拟议的PSPN模型有效地整合了各种功能,用于强大的情绪分析.
- 这种方法为复杂的人类情绪提供了更全面的表现.
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