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.
翻译:承认来自复杂、多变和非静止电子脑物理学时间序列的人类情感是影响性大脑-计算机界面的关键所在。然而,由于连续贴上不断变化的情绪状态标签在实践中并不可行,现有方法只能在一个不断的情感激发试验中为所有电子脑时间点指定固定标签,这种试验忽视了高度动态的情感状态和高度非静止的 EEEG信号。为了解决高度依赖固定标签和对时间变化信息的无知的问题,我们在本文件中提议利用深度强化学习(TAS-Net)建立一个时间觉悟抽样网络(TAS-Net),用于不受超常情感识别,从而能够探测关键情感碎片,而忽略不相关和误导的部分。对三个公共数据集(SEEED、DEAP和MAHNOB-HCI)进行了广泛的实验,以便使用离子单向外交叉校验,结果表明拟议方法优于先前的不超强的情感识别方法。