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Stochastic Multichannel Ranking with Brain Dynamics Preferences
Neural Computation ( IF 2.7 ) Pub Date : 2020-08-01 , DOI: 10.1162/neco_a_01293
Yuangang Pan 1 , Ivor W Tsang 1 , Avinash K Singh 1 , Chin-Teng Lin 1 , Masashi Sugiyama 2
Affiliation  

A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.

中文翻译:

具有脑动力学偏好的随机多通道排名

驾驶员精神疲劳的认知状态会显着影响其驾驶性能,更重要的是影响公共安全。以前的研究利用反应时间 (RT) 作为心理疲劳的指标,旨在使用回归模型中的脑电图 (EEG) 信号估计 RT 的确切值。然而,由于 RT 在数据收集过程中容易损坏且不平滑,因此专注于预测噪声测量的确切值的方法通常具有较差的泛化性能。考虑到人类 RT 是大脑动力学偏好 (BDP) 的反映,而不是 EEG 信号的单一回归输出,我们针对多通道排序问题提出了一种新颖的通道可靠性感知排序 (CArank) 模型。CArank 稳健地使用 EEG 数据从 BDP 中学习,旨在保留与 RT 对应的排序。特别是,我们引入了一个转换矩阵来表征 EEG 数据中使用的每个通道的可靠性,这有助于仅从信息丰富的 EEG 通道中学习 BDP。为了处理大规模 EEG 信号,我们提出了一种随机广义期望最大值 (SGEM) 算法以在线方式更新 CArank。对来自 40 名参与者的 EEG 信号的综合实证分析表明,我们的 CArank 实现了可靠性的实质性改进,同时检测到嘈杂或信息量较少的 EEG 通道。这有助于仅从信息丰富的 EEG 通道中学习 BDP。为了处理大规模 EEG 信号,我们提出了一种随机广义期望最大值 (SGEM) 算法以在线方式更新 CArank。对来自 40 名参与者的 EEG 信号的综合实证分析表明,我们的 CArank 实现了可靠性的实质性改进,同时检测到嘈杂或信息量较少的 EEG 通道。这有助于仅从信息丰富的 EEG 通道中学习 BDP。为了处理大规模 EEG 信号,我们提出了一种随机广义期望最大值 (SGEM) 算法以在线方式更新 CArank。对来自 40 名参与者的 EEG 信号的综合实证分析表明,我们的 CArank 实现了可靠性的实质性改进,同时检测到嘈杂或信息量较少的 EEG 通道。
更新日期:2020-08-01
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