Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: This study proposed using meta-learning algorithm Reptile to initialize the model in an EEG motor imagery classification task. The proposed Reptile-EEGNet method outperforms the EEGNet baseline. 
strengths_and_weaknesses: Strengths

1. The experiment design is valid. The proposed Reptile-EEGNet method outperformed the EEGNet baseline. 
			
Weaknesses

1. Reptile is the key innovation in this work but it is unclear how Reptile works.

2. There is no code provided to replicate the result. 

3. The presentation can be improved. (see suggestions below)
suggested_changes: Suggestions

1. I suggest authors to proofread the manuscript prior to submission. The first paragraph in abstract is redundant. The appendix section is not updated. A URM statement is needed. 

2. There are many studies working on EEG motor imagery classification task. The authors should discuss related literature more comprehensively.

3. EEGNet should be cited.

4. It's better to include details of EEG signals, like number of channels, length of time courses. 

5. Please consider adding technical description about the Reptile algorithm. 

6. Please consider adding quantitative summary in results section.

7. In Table 1, specify accuracy in % and the exact number for "many". It is more visually straight-forward if better performance is highlighted in bold.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The authors propose a novel method for transfer learning for EEG decoding. In particular, they compare their method (Reptile meta-learning) to a traditional transfer learning approach.
strengths_and_weaknesses: The authors provide an interesting method to approach a well-motivated and important problem in BCI. They clearly explained the problem and why it is a difficult one, as well as some existing approaches. Their methodology is well explained for the most part (see below). They also presented a table of results and explain that their method outperforms the current state of the art for certain numbers of fine-tuning shots.

The main weaknesses of this paper are that some of the writing is unclear, and the specific learning approach could be better explained to make the method more reproducible. Please see the suggested changes section for more specific feedback.
suggested_changes: Reproducibility:
- I think the methodology section could be built out a bit more. If there is a space issue to fit within two pages, consider getting rid of the section headers for 3.1 and 3.2, since paragraph breaks could also work well. The general three-stage approach and section 3.1 are clear, but I have trouble understanding section 3.2. A short explanation of what the Reptile algorithm is doing (what it takes in, what it modifies, etc) would be helpful here and make the method as a whole more reproducible. It would also be hugely beneficial for the authors to release their code if accepted.

Clarity: 
- The abstract currently contains the same first two sentences repeated, once this is remedied it could clear up space for later sections
- The citations are rendering right next to the text which is a bit distracting
- Some sections could use edits for clarity. In particular, I found the end of the related work section to be slightly unclear.
- The results are much appreciated and show the strength of their method in certain contexts. However, visually it could be nice to use something like a bar plot instead, with some way to highlight where their method is doing well. If the authors prefer the table, bolding the number of the better performing method would make reading it easier.
review_title: null

Meta-review: metareview: The application of a meta-learning algorithm (Reptile) to the problem of decoding imagined movements resulted in improved accuracy on unseen data from new subjects. This is important because of the desirability of the higher accuracies while only using a relatively simple technique.

The reviewers seem to agree that the writing of this paper needs to improve to include more details for clarity and reproducibility. For example, the main algorithm used in the study (Reptile) is not sufficiently introduced or explained. The code doesn't seem to have been made available. There are also stylistic issues and the text needs to be edited and proofread. The authors must be clear about their contribution and that they are applying an existing algorithm (Reptile) to a new problem, while also sufficiently detailing the solution in a clear and step-by-step way, every step of which logically builds on the previous.

confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to archive