From: ~Ransalu_Senanayake1
comment: This paper provides a comparison of spatio-temporal aspects of the feedback-related brain activity by considering the space and time positive-definite covariance matrices that lie on a Riemannian manifold.

1. What did you learn from reading this paper that you did not know before?
Distance computations in the Riemannian manifold

    2. What was the most exciting part of the work?
The comparison of various methods is interesting. However, the discussion on how and where to use these methods is not thorough enough.

    3. What in your opinion makes this work stand apart from other papers in this area?
Combining spatial and temporal features.

    4. Does the paper fit into the scope of the workshop?
Yes.

    5. Are you aware of relevant literature that you may want to make the authors aware of?
Krystal and Prado, "New methods of time series analysis of non-stationary EEG data: eigenstructure decompositions of time varying autoregressions."

Other questions: It is important to discuss nonstationarity of EEG signals in depth. The reason for choosing LDA is not clear. I believe a model such as QDA would provide better results.

Minor issues:  
*Avoid contractions "didn’t" (line 64)
*Using block letters is the standard to represent size of a vector e.g. X_i \in \mathbb{R}^{C \times T} (line 54)
*Be consistent with spaces e.g. "definite(SPD)" (line 101)
title: From: NIPS 2018 Workshop Spatiotemporal Paper37 AnonReviewer4

From: ~Mahta_Mousavi1
comment: Thank you for your constructive review.  We have updated the text to address your concerns. The number of time samples is 450 which is larger than the number of channels, i.e. 64. The regularization parameter is chosen arbitrarily but small. We didn’t test for other values. 

The choice of linear classifiers is because EEG data is noisy and these classifiers generally perform well and generalize better. For FB-CSP, the classifier choice in the literature is LDA as the log of the variance of EEG signal passed through CSP filters is close to Gaussian and LDA is a reasonable choice. A similar argument goes for FB-CTP. However, for the rest of the methods, there’s not enough evidence as the features are distributed according to a Gaussian distribution so we selected logistic regression for classification. 

The reason FB-CSP-CTP is doing better than FB-DRM-TS is possibly because CSP is a very effective well developed way to remove the high amount of spatial (between electrodes) correlation in EEG data and it ppears to be good pre-processing for future temporal analysis. The Riemannian methods are promising but the details of how to incorporate them in an end-to-end classifier are still under development. Importantly in both the Riemannian methods and the CSP/CTP methods combing spatial and temporal information improves performance.
title: Regarding AnonReviewer3's Evaluation

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper37/AnonReviewer3
evaluation: This paper proposes covariance based spatiotemporal-temporal features for the classification of feedback-related brain activity in motor imagery brain-computer interface systems. The research work presented appears to be original and technically sound. The paper is well readable. A detrimental factor in reading the paper is inconsistent writing style, i.e., there is a mix of past impersonal voice, past active voice, present active voice throughout the paper. The interpretation of the results is meaningful and honest, but not thoroughly discussed. The argument for the further work to be undertaken seems to be justified and apt. 
Few comments to consider are:
The justification for the choice of the classifier is missing. In section 2.1, we suddenly learn that number of channels is smaller then the number of time samples. It would have been good to mention the actual number of time samples here. Further, it is not clear as how was the regularisation parameter chosen? Was it chosen empirically or was it an arbitrary choice? Section 4 appears more to be a "Conclusion" section rather then "Discussion" . Perhaps, it would be good to discuss the results in section 3 and conclude the paper in section 4. I would be interested in the reason of outperformance of "FB-CSP-CTP" compared to other methods, especially "FB-DRM-TS", which is currently missing.

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper37/AnonReviewer3
relevance: 4: high
impact: 3: borderline
novelty: 4: high