PONE-D-24-09412Deep Learning-based Stress Detection for Daily Life Use Using Single-Channel EEG and GSR in a Virtual Reality Interview ParadigmPLOS ONE

Dear Dr. Cho,

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PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: No

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This manuscript describes the use of different deep learning models to identify stress and clam states using EEG and GSR signals. The manuscript is well written but the authors should address the following questions and comments?

1. Why is the learning rate of the multi-column model 10 times smaller than the single column models?

2. In the current setting, the multi-column model was initialized from the optimized single column models. Does the performance vary significantly if the multi-column model was instead initialized with the same Image-Net weights and trained using the same learning rate as the single column models?

3. The authors used spectrograms to convert time-series input signal to a 2D image for the CNN training. Has the authors considered any other time-frequency analysis methods, especially the continuous wavelet transform (CWT)? CWT has seen great success in preparing 2D data for CNN-classifiers in many other fields, for example:

https://doi.org/10.1121/1.3384871

https://doi.org/10.1115/IMECE2023-112407

4. The authors used a 5min averaging window to smooth out the predictions. Is this window length too much, considering that the interview session is only 10-15mins?

5. The training time of each model should be reported.

Reviewer #2: The authors stimulated people's stress state through virtual interviews, assessed people's stress state using questionnaires, and modeled people's stress state through EEG signals and GSR signals. Demonstrated that neural networks can assess human stress levels from both models. Here are comments:

1. By using VAS questionnaire as the evaluation method. How often did the subjects be test by the VAS? What is the granularity of the labeling?

2. GSR and EEG are used as features. How are they combined in the networks? Is normalization needed?

3. The last sentence of Section 2.2 is missing a period, and it seems like the sentence isn't complete.

4. Why are there fewer normal samples compared to stress samples? It should be easier to collect normal samples.

5. The dataset partitioning method should be introduced. If a patient appears in both the training and testing sets, it could introduce bias into the results.

6. In Section 2.4, all models were pretrained on ImageNet. Why was this done? There's a significant difference between spectrogram and ImageNet images.

7. Section 2.4 and Figure 3 introduced the methods used, including the development of the multiple-column CNN model. However, the Vision Transformer does not belong to CNN. The author should clarify how it was incorporated.

8. Are hyperparameters such as batch size and learning rate the same across all models?

9. Why was a 5-minute interval chosen for smoothing in Figure 5?

10. AUC alone is not sufficient. Other metrics should be provided for comparison.

11. What is the purpose of Figure 7? What information can we gain from it? GradCAM operates in a two-dimensional space, but the original EEG and GSR signals are one-dimensional. Is this approach reasonable?

12. Data and code should be made publicly available.

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Reviewer #1: No

Reviewer #2: No

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Deep Learning-based Stress Detection for Daily Life Use Using Single-Channel EEG and GSR in a Virtual Reality Interview Paradigm

PONE-D-24-09412R1

Dear Dr. Cho,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Xiaohui Zhang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thank the authors for addressing reviewers' comments.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed the reviewer's comment sufficiently. This manuscript can be recommended for publication.

Reviewer #2: Thanks for the author's reply. I think they have answered my questions and made corresponding changes.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

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