Kati8/ICLR_2023_2025 huggingface dataset:
authors: Elnaz Lashgari
authors: Mohamamd Salehan
authors: Ali Lashgari
reviews: 
Comments: The work proposes an architecture combining a CNN-based embedding module and relation module for a few-shot learning on motor imagery-based eeg classification. The authors benchmark the performance on 2 public datasets and 1 experimental dataset of motor imagery using accuracy and domain adaptation accuracy metrics.
Strengths: Work addresses the appropriate need for user-specific variability in EEG data and chooses to experiment with comparatively less explored approach of the few-shot learning.

The paper is decently written and easy to read and interpret.
Weaknesses: The work doesn't cite a very similar approach by An et al. (2023). However, the authors cite work from An et al. from 2020. Authors must elaborate on their novelty and benchmark performance against similar approaches to claim state-of-the-art performance on few-shot learning. 
Link to the work by An et al. (2023)
https://ieeexplore.ieee.org/abstract/document/10167679/?casa_token=ffiyMyxrlIYAAAAA:XHnQorLPEOuFdPLMhuSnkOj18y4baOutFkRqO4Zu6J1N2pKEBdsQ0cN0PvtXe3_M9R3VZvL1deH3

EEG tends to have high noise and authors though cite this concern and also claim interpretability mentioning: "A key advantage of this model is its interpretability", do not share any results, comment or compare the neurophysiological basis of the model predictions. 


Ethical guidelines while collecting personal data need to be clarified. Details on the code of ethics before releasing the data are necessary but missing.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 3
Clarity: 2
reviews: 
Comments: The authors investigate effective few-shot learning algorithms for EEG-based motor imagery classification. Specifically, transfer learning and data augmentation techniques are employed to achieve superior performance. Additionally, a meta-learning-based framework, termed Few-Shot Learning for Motor Imagery Classification, is presented for classifying unseen subject categories in a few-shot setting. Overall, this work lacks novelty and motivation and also requires further experimentation.
Strengths: The authors introduce a relation network-based meta-learning framework for EEG-based motor imagery classification.
Weaknesses: (1)	Motivation – The meta-learning computational framework focuses on “learning to learn” various tasks (meta training) that contribute to downstream tasks (meta testing). During the meta training, the model’s goal is to uncover common patterns among these tasks and acquire broad knowledge that can be applied in solving new tasks. However, the authors apply very few tasks, specifically left and right hand classification, and these tasks are directly related to downstream applications. The stated motivation for meta-learning is somewhat limited. The authors should clearly indicate how they train their framework in a meta-learning fashion. 

(2)	Motivation – The authors propose to use few-shot learning, where the model should be trained on very limited data. However, the authors only use the few-shot examples as the “support set” during the testing phase, which could leak classification information.

(3)	Related Works – Many related works are indeed missing in the field, such as Hou et al., GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals, In IEEE TNNLS.

(4)	Experiments – The authors are encouraged to conduct experiments on larger benchmarks, such as the PhysioNet dataset and the High Gamma dataset.

(5)    The authors should provide high-quality figures.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 2
reviews: 
Comments: The authors present a few-shot learning framework for motor imagery (MI) classification. This framework begins with a CNN that embeds signals from different electrodes independently. This is followed by an attention module that combines information across channels. Finally, a CNN+FC network computes relation scores between pairs of examples, allowing to retrieve the closest example from a query set. 
The framework is evaluated on two publicly available datasets, and the authors also introduce and evaluate on a novel MI dataset.
Strengths: The authors have collected a novel MI dataset and have indicated that they will make it publicly available. This contribution is a valuable new resource for the community.
In addition, data scarcity is a common issue in BCI, and the authors address this challenge by introducing their few-shot learning framework.
Weaknesses: Major
- The performance of the few-shot learning framework “RelationNet-attention” does not seem competitive because the baseline “CNN-attention-All”, which is trained on the same data as RelationNet-attention but without using examples from the test subject, systematically performs better. The difference in accuracy seems significant as it is systematically greater than 10%.

Minor
- Figure 3 misses its x-axis.
- The method is evaluated on only two benchmark datasets. The claims could be strengthened by conducting experiments on additional datasets. A large collection of MI datasets can be found in the MOABB library (http://moabb.neurotechx.com/docs/dataset_summary.html).
- The acronym DA is used both for “data augmentation” and “domain adaptation”. 
- The “domain adaptation accuracy” is not defined.
- As I understand, “CNN-attention-relation” and “RelationNet-attention” refer to the same model. To improve readability, I would recommend using a single name throughout the paper.
- The quality of the figures and diagrams can be improved.
- In my opinion, the writing could be improved to better guide the reader through the method.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2
reviews: 
Comments: This paper introduces a Few-Shot Learning (FSL) framework that incorporates feature embedding, attention, and relation modules for the classification of unseen subject categories using a limited number of labeled samples. The attention mechanism highlights important features relevant to the query data, while the relation module predicts the labels for the query by analyzing the relationships between support and query data across different subjects. The authors demonstrated the effectiveness of the proposed framework on two benchmark datasets as well as their own dataset.
Strengths: This research enhances the understanding of machine learning applications in EEG and emphasizes the potential of FSL techniques to address the challenges posed by limited training data in Motor Imagery (MI) classification.
Weaknesses: There is no substantial innovation in proposed method combining the existing approaches without any significant modifications.

No comparisons were conducted with existing state-of-the-art methods that have addressed the same issue by leveraging meta-learning, domain adaptation/generalization, etc.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 1
reviews: 
Comments: This paper examines few-shot learning methodologies applied to EEG-based motor imagery classification through transfer learning and data augmentation approaches. The authors propose a meta-learning framework called Few-Shot Learning for Motor Imagery Classification designed to handle classification of novel subject categories under few-shot constraints. However, the work demonstrates limited novelty and lacks clear motivation, and would benefit from more comprehensive experimental validation.
Strengths: The authors present a relation network-based meta-learning approach tailored for EEG-based motor imagery classification tasks.
Weaknesses: (1) Motivation – The meta-learning paradigm emphasizes learning generalizable patterns across multiple tasks during meta-training to facilitate rapid adaptation on novel meta-testing tasks. Nevertheless, the authors employ an extremely limited number of tasks (specifically binary left versus right hand discrimination), and these tasks are intrinsically connected to the target application domain. This raises questions about whether the meta-learning motivation is adequately justified. The authors should provide transparent explanation of how their framework implements meta-learning principles.

(2) Motivation – The core premise of few-shot learning is to train models with minimal training data. Yet the authors restrict few-shot examples to the support set during evaluation phases only, potentially introducing information leakage into the classification process.

(3) Related Works – The literature review overlooks several relevant contributions in this domain, including Hou et al.'s GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals, published in IEEE TNNLS.

(4) Experiments – The evaluation would be strengthened by testing on standard large-scale benchmarks such as the PhysioNet dataset and the High Gamma dataset.

(5) Presentation – The paper requires improved figure quality for better clarity and readability.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 2