Kati8/ICLR_2023_2025 huggingface dataset:
authors: Shruthi Narayanan Vaniya
authors: Sheik Mohammed Shariful Islam
authors: Maia Angelova
reviews: 
Comments: This paper argues that Major Depression Disorder (MDD) should be diagnosed using a single EEG channel instead of the full electrode channels. However, I'm seriously concerned about the lack of contribution to this work. This conference is neither a medical nor a domain-specific journal for Major Depressive Disorder. I strongly recommend referring to the BCI paper presented at last year's ICLR (Jiang et al.), which offers a more relevant perspective.

[1] Large brain model for learning generic representations with tremendous EEG data in BCI, ICLR 2024
Strengths: None.
Weaknesses: 1. None of the techniques proposed for single-channel selection:
The paper emphasizes the importance of single-channel selection from the title itself, yet it does not propose any method for selecting the channels. Instead, the authors used predefined channels and measured performance based on those. It is surprising that the paper does not consider commonly used methods for channel selection such as Hyperparameter Optimization (HPO), Layer-wise Relevance Propagation (LRP), or attention maps.
2. Lack of novelty of the model:
The paper uses a very generic CNN structure (3 layers of CNNs) without any optimization techniques specifically targeting single channels. 
3. Weak Results section:
There are no competing methods presented in the results. Where are the competing methods mentioned in the Introduction? 
4. Meaningless performance listing in the Introduction:
In the Introduction, the authors list various competing methods and their accuracies, but this serves no meaningful purpose. Simply listing accuracies from different papers applied to different datasets is not insightful. 
5. Limited usage of the dataset (58 samples)
Recommendation: 1
Reviewer confidence: 4
Soundness/correctness: 1
Clarity: 1
reviews: 
Comments: The submitted manuscript explains the approach of Major Depressive Disorder detection with effective single channel EEG signal. Authors made an attempt to pick the efficient and potential channel for the mental health monitoring.
Strengths: Addressing the potentiality of using single channel EEG in real-time implementation.
Weaknesses: 1. Lack of novelty!! In specific authors doesn't propose application dedicated features or tailored network configuration. Simply utilised the deep learning models to classify. 
2. Recent publications proved that the understanding the underlying connectivity among the channels can give the gain in depression prediction efficacy. For instance ("Delaunay Triangulated Simplicial Complex Generation for EEG Signal Classification", "GM-VRC: Semantic Topological Data Ensemble Approach for EEG Signal Classification", "Chromatic Alpha Complex Generation for EEG Signal Classification"). 
3. Due to the high non-stationarity of the EEG signal behaviour, single channel utilization in neurological/ mental disorder identification is an uncertain task.
4. Presented manuscript lacks the presentation skills of the results obtained.
5. State of arts comparison with the irrelevant or out-dated articles, can be updated with the latest and advanced techniques of depression detection.
6. Can improve the presentation quality of the manuscript.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 2
reviews: 
Comments: The paper Optimized Single EEG Channel Selection for Detecting Major Depressive Disorder proposes a CNN-based approach for diagnosing MDD using single-channel EEG data, aiming to support wearable technology for mental health monitoring. The authors test several EEG channels from different brain regions and find that specific channels, particularly from the frontal, central, and occipital regions, yield high classification accuracy (up to 88%) in differentiating MDD from non-MDD subjects. This approach prioritizes minimal electrode usage, aiming to make MDD monitoring feasible for home settings.
Strengths: 1) Applicability to Wearable Tech: Single-channel EEG setups have potential in practical applications, and the study aims to address this need without the need to go bald you can classify Major Depressive Disorder.
2) Clear Writing: The methodology and CNN architecture design are explained concisely
Weaknesses: The study’s impact is limited by its single dataset evaluation and absence of critical classification metrics :
1) Omission of Key Metrics: Essential performance metrics (F1-score, precision, recall and AUC-ROC curves) are missing, reducing evaluation comprehensiveness.
2) Underperformance: The model’s 88% accuracy is lower than Bachmann et al. (2018), which achieved 92% with a classical machine learning approach on channel Pz. And what are further advamatges of 
3) Single Dataset Limitation: Evaluating on only one dataset limits generalizability, especially when other datasets like MODMA are available.
4) Lack of Comparison: Similar single-channel or CNN-based approaches, such as DOI:10.1142/S0219622019500342 and https://doi.org/10.1177/1550059420916634, could serve as useful baselines but are not compared.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 2
Clarity: 2
reviews: 
Comments: The authors suggest an optimized single EEG channel selection and a simple convolutional deep learning model to detect Major Depressive Disorder. The authors plan to test the detection model with a single dataset input and clearly intend it to succeed in orders of magnitude involving less number of channels of EEG.
Strengths: Simplicity: The authors put forward a simple model, whereby only one channel is used to perform the EEG recordings. This may decrease the complexity as well as the cost related to the acquisition of such data in clinical or practical settings.

Focused Application: The concentrated nature of this work on the channels minimalism and application on MDD with mentioning of literature on how MA design becomes an issue while assessing patients makes sense.
Weaknesses: Lack of novelty: There are no original components both in methodology and application. The employment of a single EEG channel does not come as exciting or new, and neither is there any evidence of improvements or breakthroughs in model structure or methodology that would enhance the discipline.

No comparative analysis: The study does not provide any analyses on other EEG-based MDD detection systems that are even more advanced in deep learning or more fundamental based on machine learning algorithms. Such a comparative analysis would enable positioning of the proposed MDD approach within the competitive scientific market and its merits or demerits would be illustrated.

Limited dataset: The finding cannot be said to be fully conclusive since only one dataset was employed. Testing the model against several datasets would add weight to the evidence of the model application in different settings and sociocultural characteristics of the population.

Insufficient performance gains: The paper does not exhibit any significant progresses relative to the three computed parameters such as the model accuracy, computation time, and clinical importance. In the absence of these parameters, the proposed approach does not provide credible case for progress in the field or major contributions in the area of MDD detection.
Recommendation: 3
Reviewer confidence: 1
Soundness/correctness: 3
Clarity: 3