reviews: 
Comments: **Overall Assessment**

The paper addresses a significant problem in EEG motor imagery classification using few-shot learning. However, the novelty of the research question and the approach remain limited, which affects the contribution and impact of this study.
Strengths: **Key Strengths**

* Problem Significance: EEG-based MI classification for BCI applications is a highly relevant research area, especially given the challenges of data scarcity and cross-subject variability.
* Dataset Contribution: The authors introduce a new dataset specific to their experimental needs, potentially serving as a resource for further MI EEG research.
* Attention Mechanism and Data Augmentation: The use of an attention mechanism to enhance feature extraction and a data augmentation strategy to improve classification accuracy aligns well with current trends in EEG and time-series signal analysis.
Weaknesses: **Major Concerns and Areas for Improvement:**

1. Limited Novelty in Approach and Research Question
    * While the problem is essential, the paper does not introduce significant advancements in methodology or approach, primarily adapting existing frameworks for few-shot learning.
    * The techniques, including data augmentation and attention mechanisms, are well-known and lack customization to the problem at hand.
2. Insufficient Literature Review
    * The manuscript leans heavily on a limited set of cited works, neglecting a broader body of relevant and foundational literature. This oversight is evident, for example, in the omission of a citation for the seminal paper on the attention mechanism (Vaswani, A. "Attention is all you need." (2017)), which is essential for context or the omission of the Grad-CAM paper (Selvaraju, Ramprasaath R., et al. "Grad-cam: Visual explanations from deep networks via gradient-based localization." (2017)), or proper citation to the utilized baseline methods, etc.
    * The limited literature results in redundancy, where the few sources cited appear multiple times, reducing the depth of the discussion.
3. Redundant Content and Limited Focus on Methodology
    * A large portion of the paper is dedicated to reintroducing prior works and discussing the dataset, with limited space allocated to details of the proposed method.
    * The method's description lacks sufficient depth to fully understand its contribution beyond existing frameworks, making it challenging to assess its true impact.
4. Results and Experimental Design
    * The reported results do not demonstrate outperformance over baseline models (CNN-attention-All and CNN-attention-Few). This lack of improvement questions the validity of the proposed framework as a state-of-the-art advancement in EEG classification.
    * It is unclear why the authors have not tested their own method using a 40-sample case, as they did with CNN-attention baselines. Including this setup would provide a more equitable basis for comparison, potentially even enhancing the own results. The authors do not address any limitations that might prevent this configuration, leaving the rationale for this decision unclear.
    * The experimental design could be expanded to assess the model’s performance to a broader range of baseline methods.
  5. Interpretability of Attention Mechanism
One of the noted strengths of the proposed framework is its attention mechanism. However, while the authors suggest interpretability as a benefit, no specific analysis or visualization is provided to demonstrate how the attention scores contribute to understanding EEG signal dynamics. Adding such interpretability analysis would clarify the attention module’s effectiveness in isolating relevant features in EEG data.


**Minor Concern:**

Presentation and Figure Quality: The quality of figures is low, which detracts from the visual clarity and effectiveness of the results. Enhancing figure resolution would improve the readability and professional presentation of the study.

**Recommendation for Extended Testing on Diverse Tasks:**
Given the framework's potential for adaptation beyond EEG data, the authors could include further testing on additional EEG classification tasks or even generalize their method to other time-series datasets. This would reinforce the flexibility and generalizability of the FSL-MIC model and provide a more robust foundation for the claimed broader applicability.


The paper requires major revisions, including a more comprehensive literature review, expanded experiments, and detailed methodology. Enhancing the experimental setup and introducing a wider array of baselines could make this work more impactful.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2