Emotion Elicitation through Vibrotactile Stimulation as an Alternative for Deaf and Hard of Hearing People: An EEG Study

Round 1

Reviewer 1 Report

The article is more inclined to review but research, the organization is not good. Some recent articles are missing. Viability and Applicability of Deep Learning Approach for COVID-19 Preventive Measures Implementation, An ECG Heartbeat Classification Strategy using Deep Learning for Automated Cardiocare Application, Voting Classification Method with PCA and K-Means for Diabetic Prediction, A Technique for Human Upper Body Parts Movement Tracking, Classification and detection of citrus diseases using deep learning, Predictive analytics for recognizing human activities using residual network and fine-tuning

Author Response

Response to Reviewer 1 Comments

Point 1: The article is more inclined to review but research, the organization is not good. Some recent articles are missing. Viability and Applicability of Deep Learning Approach for COVID-19 Preventive Measures Implementation, An ECG Heartbeat Classification Strategy using Deep Learning for Automated Cardiocare Application, Voting Classification Method with PCA and K-Means for Diabetic Prediction, A Technique for Human Upper Body Parts Movement Tracking, Classification and detection of citrus diseases using deep learning, Predictive analytics for recognizing human activities using residual network and fine-tuning.

Response 1: We thank the Reviewer for pointing out these concerns.

Firstly, we would like to stress that we present a set of new results after a detailed experiment, and we humbly consider that, therefore, the paper cannot be considered as a review one. After the introduction, we have settled the basis of our experiment both by describing the sample of participants and the setup that we built, consisting not only of the EEG measurements but also of the electronically driven gloves that provided the stimuli. Then, we depict the whole experimental procedure in detail, particularly describing how the stimuli were given to the participants. After showing the results measured by the EEGs, we discuss them in the light of the neurological connections that could be established, and extracting the main conclusion of the paper: emotions can be elicited by our particular and synchronized haptic stimulation.

On the other hand, we thank the Reviewer for these mentioned papers, as they give us the opportunity to improve the bibliography of our work. We have included them, as well as a new paragraph n the introduction to stress their relevancy in studies like the one that we present. The paragraph is:

While in our study we have preferred the use of EEG measurements and their inversion procedures because of its efficiency and low cost, it is worth to mention that there are also other Human-Machine Interfaces (HMI) such as Brain-Computer Interfaces (BCI) and eye-tracking based methodologies [19]. These techniques have provided strong coupling between the results of a cognitive psychological attention test and the attention levels determined by BCI systems. That was the case of an examination and comparison between an EEG-based attention test and a Continuous Performance Test (CPT), as well as a Test of Variables of Attention (TOVA) [20], or different comparisons with other human-computer interaction eye movement tracking [21-24]. On the other hand, some interesting results are also obtained when applying deep learning techniques in inversion problems for eye tracking and other measurements [25-30].

Being the new References:

19. J. Katona, T. Ujbanyi, G. Sziladi and A. Kovari, "Examine the effect of different web-based media on human brain waves," 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), 2017, pp. 000407-000412, doi: 10.1109/CogInfoCom.2017.8268280.
20. Katona, J., & Kovari, A. (2018). The evaluation of bci and pebl-based attention tests. Acta Polytechnica Hungarica, 15(3), 225-249.
21. Katona, J., Kovari, A., Heldal, I., Costescu, C., Rosan, A., Demeter, R., ... & Stefanut, T. (2020, September). Using eye-tracking to examine query syntax and method syntax comprehension in LINQ. In 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom) (pp. 000437-000444). IEEE.
22. Katona, J. (2022). Measuring Cognition Load Using Eye-Tracking Parameters Based on Algorithm Description Tools. Sensors, 22(3), 912.
23. Katona, J. (2021). Clean and dirty code comprehension by eye-tracking based evaluation using GP3 eye tracker. Acta Polytechnica Hungarica, 18(1), 79-99.
24. Katona, J. (2021). Analyse the Readability of LINQ Code using an Eye-Tracking-based Evaluation. Acta Polytech. Hung, 18, 193-215.
25. Negi, A., & Kumar, K. (2022). Viability and Applicability of Deep Learning Approach for COVID-19 Preventive Measures Implementation. In International Conference on Artificial Intelligence and Sustainable Engineering (pp. 367-379). Springer, Singapore
26. Sheikh, D., Vansh, A. R., Verma, H., Chauhan, N., Kumar, R., Sharma, R., ... & Awasthi, L. K. (2021, December). An ECG Heartbeat Classification Strategy using Deep Learning for Automated Cardiocare Application. In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) (pp. 515-520). IEEE.
27. Yadav, A., Verma, H. K., & Awasthi, L. K. (2021). Voting Classification Method with PCA and K-Means for Diabetic Prediction. In Innovations in Computer Science and Engineering (pp. 651-656). Springer, Singapore.
28. Kumar, K., Mishra, A., Dahiya, S., & Kumar, A. (2022). A Technique for Human Upper Body Parts Movement Tracking. IETE Journal of Research, 1-10.
29. Negi, A., & Kumar, K. (2021). Classification and detection of citrus diseases using deep learning. In Data science and its applications (pp. 63-85). Chapman and Hall/CRC.
30. Negi, A., Kumar, K., Chaudhari, N. S., Singh, N., & Chauhan, P. (2021, December). Predictive analytics for recognizing human activities using residual network and fine-tuning. In International Conference on big data analytics (pp. 296-310). Springer, Cham.

Author Response File: Author Response.pdf

Reviewer 2 Report

I enjoyed reading the paper. The authors have presented an interesting article about an Emotion Elicitation through Vibrotactile Stimulation as an Al-ternative for Deaf and Hard of Hearing People: An EEG Study .

Abstract, overview
The abstract is a concise description of the work. The introduction is well structured, and it covers all the concepts investigated in the methodological part. The previous work is well presented and integrated. I consider that this work brings added value in the field and the specific objectives of the manuscript are well related to the previous work developed in this domain. 

Methodology
The research design used is appropriate in order to answer the research questions proposed by the authors. The methods are described properly. The results are clearly presented and are in relation to the concepts investigated.

Discussion and conclusions
The discussions are clear and concise. The conclusions are strongly related to the findings of the research work.

Format and style
All the format and style features were respected and are compliant with the requirements.

References
The format of the reference list fixes well to the specified format.

Plagiarism and any other ethical concerns about this study
I do not have any potential conflict of interest with regards to this paper.

Despite the good work done, there is still some room for improvement, as follows:

• I think some more literatures should be added. Besides the mentioned HMI there are several other systems (like cost-effect BCI, eye-tracking) which are applied nowadays. It would be good to see the "effect of different web-based media" content on "human brain waves", as well as the additional applications of brainwave-based control like in examine the effect of different web-based media on human brain waves. It would improve the quality of the publication to mention the relationship between a cognitive psychological attention test and the attention levels determined by a BCI systems such as in an examination and comparison of the EEG based attention test with CPT and TOVA. In addition to BCI systems, mentioning other important human-computer interaction eye movement tracking would also improve quality, as such systems can be used in the analysis of programming technologies such as LINQ and algorithms, thus enabling, for example, cognition load or source code, algorithm description tools readability testing like in measuring cognition load using eye-tracking parameters based on algorithm description tools, in clean and dirty code comprehension by eye-tracking based evaluation using GP3 eye tracker and in analyse the readability of LINQ Code using an eye-tracking-based evaluation.

Author Response

Response to Reviewer 2 Comments

Point 1: I enjoyed reading the paper. The authors have presented an interesting article about an Emotion Elicitation through Vibrotactile Stimulation as an Al-ternative for Deaf and Hard of Hearing People: An EEG Study .

Abstract, overview

The abstract is a concise description of the work. The introduction is well structured, and it covers all the concepts investigated in the methodological part. The previous work is well presented and integrated. I consider that this work brings added value in the field and the specific objectives of the manuscript are well related to the previous work developed in this domain.

Methodology

The research design used is appropriate in order to answer the research questions proposed by the authors. The methods are described properly. The results are clearly presented and are in relation to the concepts investigated.

Discussion and conclusions

The discussions are clear and concise. The conclusions are strongly related to the findings of the research work.

Format and style

All the format and style features were respected and are compliant with the requirements.

References

The format of the reference list fixes well to the specified format.

Plagiarism and any other ethical concerns about this study

I do not have any potential conflict of interest with regards to this paper.

Response 1: We thank the Reviewer for this extensive evaluation, we are glad and very proud of having reached the quality standards that have been described with such an extensive depth.

Point 2: Despite the good work done, there is still some room for improvement, as follows:

• I think some more literatures should be added. Besides the mentioned HMI there are several other systems (like cost-effect BCI, eye-tracking) which are applied nowadays. It would be good to see the "effect of different web-based media" content on "human brain waves", as well as the additional applications of brainwave-based control like in examine the effect of different web-based media on human brain waves. It would improve the quality of the publication to mention the relationship between a cognitive psychological attention test and the attention levels determined by a BCI systems such as in an examination and comparison of the EEG based attention test with CPT and TOVA. In addition to BCI systems, mentioning other important human-computer interaction eye movement tracking would also improve quality, as such systems can be used in the analysis of programming technologies such as LINQ and algorithms, thus enabling, for example, cognition load or source code, algorithm description tools readability testing like in measuring cognition load using eye-tracking parameters based on algorithm description tools, in clean and dirty code comprehension by eye-tracking based evaluation using GP3 eye tracker and in analyse the readability of LINQ Code using an eye-tracking-based evaluation.

Response 2: We thank the Reviewer for the mentioned studies, as they give us the opportunity to expand the framework in which our work must be considered, being at the same time a way to improve the bibliography of our work. We have included some new papers, as well as a new paragraph in the introduction to stress their relevancy in studies like the one that we present. The paragraph is:

While in our study we have preferred the use of EEG measurements and their inversion procedures because of its efficiency and low cost, it is worth to mention that there are also other Human-Machine Interfaces (HMI) such as Brain-Computer Interfaces (BCI) and eye-tracking based methodologies [19]. These techniques have provided strong coupling between the results of a cognitive psychological attention test and the attention levels determined by BCI systems. That was the case of an examination and comparison between an EEG-based attention test and a Continuous Performance Test (CPT), as well as a Test of Variables of Attention (TOVA) [20], or different comparisons with other human-computer interaction eye movement tracking [21-24]. On the other hand, some interesting results are also obtained when applying deep learning techniques in inversion problems for eye tracking and other measurements [25-30].

Being the new References:

19. J. Katona, T. Ujbanyi, G. Sziladi and A. Kovari, "Examine the effect of different web-based media on human brain waves," 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), 2017, pp. 000407-000412, doi: 10.1109/CogInfoCom.2017.8268280.
20. Katona, J., & Kovari, A. (2018). The evaluation of bci and pebl-based attention tests. Acta Polytechnica Hungarica, 15(3), 225-249.
21. Katona, J., Kovari, A., Heldal, I., Costescu, C., Rosan, A., Demeter, R., ... & Stefanut, T. (2020, September). Using eye-tracking to examine query syntax and method syntax comprehension in LINQ. In 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom) (pp. 000437-000444). IEEE.
22. Katona, J. (2022). Measuring Cognition Load Using Eye-Tracking Parameters Based on Algorithm Description Tools. Sensors, 22(3), 912.
23. Katona, J. (2021). Clean and dirty code comprehension by eye-tracking based evaluation using GP3 eye tracker. Acta Polytechnica Hungarica, 18(1), 79-99.
24. Katona, J. (2021). Analyse the Readability of LINQ Code using an Eye-Tracking-based Evaluation. Acta Polytech. Hung, 18, 193-215.
25. Negi, A., & Kumar, K. (2022). Viability and Applicability of Deep Learning Approach for COVID-19 Preventive Measures Implementation. In International Conference on Artificial Intelligence and Sustainable Engineering (pp. 367-379). Springer, Singapore
26. Sheikh, D., Vansh, A. R., Verma, H., Chauhan, N., Kumar, R., Sharma, R., ... & Awasthi, L. K. (2021, December). An ECG Heartbeat Classification Strategy using Deep Learning for Automated Cardiocare Application. In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) (pp. 515-520). IEEE.
27. Yadav, A., Verma, H. K., & Awasthi, L. K. (2021). Voting Classification Method with PCA and K-Means for Diabetic Prediction. In Innovations in Computer Science and Engineering (pp. 651-656). Springer, Singapore.
28. Kumar, K., Mishra, A., Dahiya, S., & Kumar, A. (2022). A Technique for Human Upper Body Parts Movement Tracking. IETE Journal of Research, 1-10.
29. Negi, A., & Kumar, K. (2021). Classification and detection of citrus diseases using deep learning. In Data science and its applications (pp. 63-85). Chapman and Hall/CRC.
30. Negi, A., Kumar, K., Chaudhari, N. S., Singh, N., & Chauhan, P. (2021, December). Predictive analytics for recognizing human activities using residual network and fine-tuning. In International Conference on big data analytics (pp. 296-310). Springer, Cham.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Accepted

Reviewer 2 Report

I accept the paper in present form.