Stochastic Multichannel Ranking with Brain Dynamics Preferences

Abstract

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.

Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation

“…From Figure 1, we find that (1) apart from a few local mispredictions, SVR can exactly predict the RT on the training trials and is insensitive to the extreme values. It proves that SVR has sufficient fitting capability for mental fatigue monitoring and has superior robustness to extreme values compared to deep regression models (Pan et al, 2020). (2) All SVR models show poor prediction performance 2 on the remaining trials.…”

“…line Applications. Some previous work derived from linear (Resalat & Saba, 2015;Lin et al, 2010) and nonlinear (Liu et al, 2016;Cui & Wu, 2017;Pan et al, 2020) methods show that it is possible to detect mental fatigue with high accuracy. It is impressive, but it would be rather blind to the wealth of the dynamics and behavioral variability (Müller et al, 2008;Ratcliff et al, 2009;Yarkoni et al, 2009;Wei et al, 2018;Cui et al, 2019).…”

“…In pursuit of online mental fatigue evaluation, computational efficiency in terms of time and memory has been a major concern. Deep learning (Goodfellow et al, 2016) methods achieve superior performance (Pan et al, 2020) but require massive training data. Riemannian methods (Barachant et al, 2012;Congedo et al, 2017) achieve good performance with a small number of training trials but incur an overhead computational cost.…”

“…In terms of data-driven mental fatigue evaluation, the reaction time (RT) to a certain assigned task is widely adopted as supervision, to indicate the fatigue level. Some linear (Lin et al, 2010;Resalat & Saba, 2015) and nonlinear (Liu, Lin, Wu, Chuang, & Lin, 2016;Cui & Wu, 2017;Pan, Tsang, Singh, Lin, & Sugiyama, 2020) methods show that it is possible to detect mental fatigue with high accuracy. It is impressive but also blind to the wealth of the dynamics and behavioral variability (Müller et al, 2008;Ratcliff, Philiastides, & Sajda, 2009;Yarkoni, Barch, Gray, Conturo, & Braver, 2009;Xu, Min, & Hu, 2018).…”

“…The brain dynamics preferences can be constructed via some objective fatigue indicator, such as RT if available, or some power spectral features (Wang et al, 2018;Bose et al, 2019). Preferences-based, indirect mental fatigue evaluation is proved to alleviate the overfitting issue of directly predicting RT in a regression task (Pan et al, 2020). In particular, SWORE models the brain's dynamic preferences in terms of two states: shaking state and steady state.…”