论文标题
无监督的时间感知抽样网络，具有深入的强化学习，以基于脑电图的情绪识别
Unsupervised Time-Aware Sampling Network with Deep Reinforcement Learning for EEG-Based Emotion Recognition
论文作者
论文摘要
从复杂，多元和非稳态脑电图（EEG）时间序列中识别人类情绪对于情感脑部计算机界面至关重要。但是，由于在实践中不断变化的情绪状态的连续标记是不可行的，因此现有方法只能在连续引起的情绪诱发的试验中为所有脑电图分配固定的标签，该试验忽略了高度动态的情绪状态和高度非平稳的EEG信号。为了解决高度依赖固定标签的问题和不间断的信息的无知，在本文中，我们建议使用深入的增强学习（DRL）提出一个时间觉醒的抽样网络（TAS-NET），以实现无治疗的情绪识别，这能够检测到关键情绪碎片并忽视不挑剔的不相关和误导性的部分。在三个公共数据集（种子，DEAP和MAHNOB-HCI）上进行了广泛的实验，以使用剩余的对象交叉验证进行情绪识别，结果证明了该方法与先前未经审查的情绪识别方法的优越性。
Recognizing human emotions from complex, multivariate, and non-stationary electroencephalography (EEG) time series is essential in affective brain-computer interface. However, because continuous labeling of ever-changing emotional states is not feasible in practice, existing methods can only assign a fixed label to all EEG timepoints in a continuous emotion-evoking trial, which overlooks the highly dynamic emotional states and highly non-stationary EEG signals. To solve the problems of high reliance on fixed labels and ignorance of time-changing information, in this paper we propose a time-aware sampling network (TAS-Net) using deep reinforcement learning (DRL) for unsupervised emotion recognition, which is able to detect key emotion fragments and disregard irrelevant and misleading parts. Extensive experiments are conducted on three public datasets (SEED, DEAP, and MAHNOB-HCI) for emotion recognition using leave-one-subject-out cross-validation, and the results demonstrate the superiority of the proposed method against previous unsupervised emotion recognition methods.
