Working memory (WM), denoting the information temporally stored in the mind, is a fundamental research topic in the field of human cognition. Electroencephalograph (EEG), which can monitor the electrical activity of the brain, has been widely used in measuring the level of WM. However, one of the critical challenges is that individual differences may cause ineffective results, especially when the established model meets an unfamiliar subject. In this work, we propose a cross-subject deep adaptation model with spatial attention (CS-DASA) to generalize the workload classifications across subjects. First, we transform EEG time series into multi-frame EEG images incorporating spatial, spectral, and temporal information. First, the Subject-Shared module in CS-DASA receives multi-frame EEG image data from both source and target subjects and learns the common feature representations. Then, in the subject-specific module, the maximum mean discrepancy is implemented to measure the domain distribution divergence in a reproducing kernel Hilbert space, which can add an effective penalty loss for domain adaptation. Additionally, the subject-to-subject spatial attention mechanism is employed to focus on the discriminative spatial features from the target image data. Experiments conducted on a public WM EEG dataset containing 13 subjects show that the proposed model is capable of achieving better performance than existing state-of-the-art methods.
翻译:工作记忆(WM),指出在思想中暂时储存的信息,是人类认知领域的一个基本研究课题。电脑摄影(EEEG)可以监测大脑的电动活动,在测量WM水平时广泛使用。然而,一个关键的挑战是,个别差异可能造成无效结果,特别是当既定模型满足一个不熟悉的主题时。在这项工作中,我们提出一个具有空间关注的横向深度适应模型(CS-DASA),以概括各学科的工作量分类。首先,我们将EEEG时间序列转换成包含空间、光谱和时间信息的多框架EEEG图像。首先,CS-DASA的主体共享模块从源和目标对象接收多框架EEG图像数据,并学习共同特征的表述。然后,在特定主题模块中,采用最大平均值差异,以测量再生产核心空间的域分布差异,这可以为区域适应增加有效的惩罚损失。此外,主题对主题的空间关注机制(EEEEG)将侧重于13项现有实验性空间目标的定位,从现有图像上显示更好的环境数据。