Driver's cognitive state of mental fatigue significantly affects driving performance and more importantly public safety. Previous studies leverage the response 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 EEG signals and also non-smooth RTs during data collection, regular regression methods generally suffer from poor generalization performance. Considering that human response time is the reflection of brain dynamics preference rather than a single value, a novel model called Brain Dynamics ranking (BDrank) has been proposed. BDrank could learn from brain dynamics preferences using EEG data robustly and preserve the ordering corresponding to RTs. BDrank model is based on the regularized alternative ordinal classification comparing to regular regression based practices. Furthermore, a transition matrix is introduced to characterize the reliability of each channel used in EEG data, which helps in learning brain dynamics preferences only from informative EEG channels. In order to handle large-scale EEG signals and obtain higher generalization, an online-generalized Expectation Maximum (OnlineGEM) algorithm also has been proposed to update BDrank in an online fashion. Comprehensive empirical analysis on EEG signals from 44 participants shows that BDrank together with OnlineGEM achieves substantial improvements in reliability while simultaneously detecting possible less informative and noisy EEG channels.
The paper proposes an algorithm for mental fatigue monitoring, relating a subjects' EEG signals to their reaction time (RT) during a simulated driving task, as an ordinal regression problem. The authors argue that RTs could be heavily skewed and/or non-smooth, making traditional regression approaches unstable due to outlier values. They propose a brain dynamic ranking algorithm,  BDrank, using a generalized EM algorithm to estimate its parameters, and compare it to support vector regression and Logistic Ordinal Regression, where they show improved performance by accuracy and root mean squared error (RMSE) over a database of 44 subjects.
General comments, in no particular order:
1. There are some minor grammatical errors throughout. The paper could benefit from another read-through to correct these errors.
2. It is unclear to me how the model works at test time; as the model is essentially building a relational structure in the data, does the user have to provide multiple EEG trials at time of prediction?
3. There is notation early on in the paper that doesn't appear to be appropriately defined. For example, Equation (1) describes two sets of propositions, with M1 and M2 elements, respectively. How is M1 and M2, the total set of propositions, calculated? It appears to be all pair-wise comparisons of RTs but then it's unclear why there are two indexes associated with them. Also, what does it mean for the orderings to be significant? (i.e.: that the "type-1 preference propositions that the orderings between the RTs are significant"). The authors then switch to a new notation x^1 and x^2 without defining them. Notational problems also persist throughout the paper, making it hard to gauge what is being done at each step.
4. The authors describe using an FFT to transform the EEG data into the frequency domain. I'm assuming they are doing the FFT on the entire 10-s interval but the paper does not make this clear. Also, the authors state using EEG power between 0-30Hz for their analysis; do they further sub-divide this range (for example, to the standard theta/alpha/beta power ranges) or just use the power across the entire 0-30Hz band?
5. I am concerned with the relatively sparse set of comparison algorithms the authors use. The authors only compare to relatively simple approaches (support vector regression and logistic ordinal regression), yet they cite many previous works in this area but do not compare against them, instead just leaving a pretty generic statement of "The regression assumption of this method between EEG signals and RT is not correct"; they do not elaborate on this aspect.
 
Overall I think there is limited novelty in the approach; the idea to learn the structure of the data relationally instead of absolutely is pretty straight-forward, and is a standard practice for example in non-parametric statistical modeling. I am also not positive that ICLR is the best venue for this work; perhaps a better avenue for this would be in a more BCI/neural engineering-focused venue.