基于面部表情、EEG和ECG信号利用基本情绪的复杂情绪识别系统：综述
Complex Emotion Recognition System using basic emotions via Facial Expression, EEG, and ECG Signals: a review
AI总结：
该综述评估了机器学习、深度学习及元学习方法结合面部表情、EEG和ECG信号在复杂情绪识别中的效能，指出研究空白，强调元学习对系统优化的价值，推动其临床应用。
摘要：
复杂情绪识别系统（Complex Emotion Recognition System, CERS）通过分析所表达的基本情绪的组合、它们之间的关联以及动态变化来解读复杂情绪状态。借助先进算法，CERS为情绪动态变化提供了深刻洞见，助力实现对情绪的细致理解与定制化响应。要让机器达到这样的情绪识别水平，需要进行知识蒸馏，并理解类似于人类认知的新型概念。开发用于识别复杂情绪的AI系统是一项巨大挑战，对情感计算具有重要意义。此外，由于捕捉细微情绪涉及诸多复杂因素，为CERS获取大规模数据集是一项艰巨任务，这就需要专门的数据采集与处理方法。引入心电图（Electrocardiogram, ECG）和脑电图（Electroencephalogram, EEG）等生理信号，能够通过提供用户情绪状态的宝贵洞见、提升数据集质量并增强系统可靠性，显著优化CERS。本研究开展了全面的文献综述，评估了机器学习、深度学习和元学习方法在利用EEG、ECG信号和面部表情数据集进行基本情绪与复杂情绪识别方面的效能。入选的研究论文呈现了CERS的潜在应用、临床意义及相关结果，旨在推动其在临床决策流程中的接受度与整合度。本研究指出了当前在CERS认知方面存在的研究空白与挑战，鼓励相关研究与机构开展进一步探索。最后，本研究强调了元学习方法在提升CERS性能及指导未来研究方向方面的重要性。
英文摘要：
The Complex Emotion Recognition System (CERS) deciphers complex emotional states by examining combinations of basic emotions expressed, their interconnections, and the dynamic variations. Through the utilization of advanced algorithms, CERS provides profound insights into emotional dynamics, facilitating a nuanced understanding and customized responses. The attainment of such a level of emotional recognition in machines necessitates the knowledge distillation and the comprehension of novel concepts akin to human cognition. The development of AI systems for discerning complex emotions poses a substantial challenge with significant implications for affective computing. Furthermore, obtaining a sizable dataset for CERS proves to be a daunting task due to the intricacies involved in capturing subtle emotions, necessitating specialized methods for data collection and processing. Incorporating physiological signals such as Electrocardiogram (ECG) and Electroencephalogram (EEG) can notably enhance CERS by furnishing valuable insights into the user's emotional state, enhancing the quality of datasets, and fortifying system dependability. A comprehensive literature review was conducted in this study to assess the efficacy of machine learning, deep learning, and meta-learning approaches in both basic and complex emotion recognition utilizing EEG, ECG signals, and facial expression datasets. The chosen research papers offer perspectives on potential applications, clinical implications, and results of CERSs, with the objective of promoting their acceptance and integration into clinical decision-making processes. This study highlights research gaps and challenges in understanding CERSs, encouraging further investigation by relevant studies and organizations. Lastly, the significance of meta-learning approaches in improving CERS performance and guiding future research endeavors is underscored.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
