Reviews: Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

In this paper, the authors present a multi-state Dynamic Recurrent Neural Network architecture and training framework for Brain Machine Interface (BMI), including incorporating scheduled sampling and testing diverse neural features as input. The authors robustly analyze this model in comparison to other prior modeling frameworks on human posterior parietal cortical activity (PPC). This paper is of an impressive quality, containing rigorous and methodical analyses showing clear and significant improvements of their model. The authors compare to twelve baseline models and investigate many aspects of the modeling framework, including single-day vs multi-day performance, generalization of single-day training to other days, the reliance on amount of training data, the optimal preprocessing of neural feature inputs, and generalization of the models over time with different styles of retraining. The paper was very well-written, with most choices and details clearly explained.