State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China.
J Healthc Eng. 2020 Nov 19;2020:8829451. doi: 10.1155/2020/8829451. eCollection 2020.
The topdown determined visual object perception refers to the ability of a person to identify a prespecified visual target. This paper studies the technical foundation for measuring the target-perceptual ability in a guided visual search task, using the EEG-based brain imaging technique. Specifically, it focuses on the feature representation learning problem for single-trial classification of fixation-related potentials (FRPs). The existing methods either capture only first-order statistics while ignoring second-order statistics in data, or directly extract second-order statistics with covariance matrices estimated with raw FRPs that suffer from low signal-to-noise ratio. In this paper, we propose a new representation learning pipeline involving a low-level convolution subnetwork followed by a high-level Riemannian manifold subnetwork, with a novel midlevel pooling layer bridging them. In this way, the discriminative power of the first-order features can be increased by the convolution subnetwork, while the second-order information in the convolutional features could further be deeply learned with the subsequent Riemannian subnetwork. In particular, the temporal ordering of FRPs is well preserved for the components in our pipeline, which is considered to be a valuable source of discriminant information. The experimental results show that proposed approach leads to improved classification performance and robustness to lack of data over the state-of-the-art ones, thus making it appealing for practical applications in measuring the target-perceptual ability of cognitively impaired patients with the FRP technique.
自上而下确定的视觉对象感知是指一个人识别预定视觉目标的能力。本文研究了使用基于 EEG 的脑成像技术在引导视觉搜索任务中测量目标感知能力的技术基础。具体来说，它专注于用于注视相关电位 (FRP) 单次分类的特征表示学习问题。现有的方法要么仅捕获数据中的一阶统计信息，而忽略二阶统计信息，要么直接使用受低信噪比影响的原始 FRP 估计协方差矩阵来提取二阶统计信息。在本文中，我们提出了一种新的表示学习流水线，涉及一个低级卷积子网，后跟一个高级黎曼流形子网，并在它们之间使用新颖的中层池化层进行连接。这样，卷积子网可以增加一阶特征的判别能力，而后续的黎曼子网可以进一步深入学习卷积特征中的二阶信息。特别是，我们的流水线中的组件很好地保留了 FRP 的时间顺序，这被认为是有价值的判别信息源。实验结果表明，与最先进的方法相比，所提出的方法可提高分类性能和对数据缺乏的鲁棒性，因此，它很有吸引力，可用于使用 FRP 技术测量认知障碍患者的目标感知能力的实际应用。
