1. Too Technical: It may be challenging for readers without a background in neuroscience or signal processing. Add some signal processing knowledge (overview everyday example). a. Mobile Communications b. Health Monitoring

and Medical Imaging c. Automotive Safety and Autonomous Vehicles 2. Focused solely on Kalman Filters: Provide some alternative or similar methods in neural signal decoding and give some reasons/examples why Kalman filters in some cases are the best way to decode the neural signal. 3. Add code and mathematical proof and provide some background knowledge related to Kalman Filters Neural Network, https://www.ibm.com/docs/en/spss-modeler/18.0.0?topic=networks-neural-mod el Math on Kalman filter https://web.mit.edu/kirtley/kirtley/binlustuff/literature/control/Kalman%20filter.p df Code: https://github.com/KordingLab/Neural_Decoding/blob/master/Examples_kf_dec oder.ipynb 4. More practical examples: a. GPS or navigation for ships, control of vehicles, and aircraft(dynamical position) b. Tracking object c. Economics d. Computer Vision Applications 5. State some restrictions or drawbacks on Kalman Filters. a. It assumes that both the system and observation model equations are linear, which is not realistic in many real-life situations. b. It assumes that the state belief is Gaussian distributed.