Development of an Application That Implements a Brain–Computer Interface to an Upper-Limb Motor Assistance Robot to Facilitate Active Exercise in Patients: A Feasibility Study
Round 1
Reviewer 1 Report
The study aimed to evaluate the effectiveness of a brain robot in rehabilitation that com- 10 bines motor imagery (MI), robotic motor assistance, and electrical stimulation
Authors provide sufficient background and include references. The materials and methods section is adequately described.
Line 204: …EEG data recorded from the patients were loaded into the DiC system, and the reaction time from the time when the analysis of ERD detection started to the time when the arm of the DiC was activated was measured. Please correct.
Line 244: Authors informed that 13 patients were included for the final analysis (Figure 5) while in the Figure 5 there is information that further exclusion have been made (due to outliers (n=1), due to machine trouble (n=3)). Please correct.
Line 272: In the MSM, patient 1 was excluded the patient was an outlier among the 13 patients included in the experiment – the sentence makes no sense
The median onset stroke in this study was 84 days (some patients were 29 days from onset, other 194). The potential of recovery (plasticity) is very different for those patients. Why did you decide to include so divergent group?
Author Response
Reviewer #1
The study aimed to evaluate the effectiveness of a brain robot in rehabilitation that com- 10 bines motor imagery (MI), robotic motor assistance, and electrical stimulation.
Authors provide sufficient background and include references. The materials and methods section is adequately described.
Comment 1.
Line 204: …EEG data recorded from the patients were loaded into the DiC system, and the reaction time from the time when the analysis of ERD detection started to the time when the arm of the DiC was activated was measured. Please correct.
Response to Comment 1
Thank you for your careful review and useful comments regarding our manuscript.
We revised the sentence as follows according to this comment:
“The EEG data recorded from the patients were loaded into the DiC, and the reaction time from when the analysis of ERD detection started to when the DiC arm was activated was measured.” Please see lines 207–209.
Comment 2.
Line 244: Authors informed that 13 patients were included for the final analysis (Figure 5) while in the Figure 5 there is information that further exclusion have been made (due to outliers (n=1), due to machine trouble (n=3)). Please correct.
Response to Comment 2
Thank you for your careful review. We have amended the sentence as follows: “In the experiment in which ERDs were detected by the MSM and RP algorithms, the data regarding the 13 patients were detected by MSM, and one patient, whose data could not be detected by RP, was excluded from the analysis. In measurements of reaction time using the DIC activated by the ERD data detected by the MSM, one patient, who was an outlier, and three patients whose reaction times could not be measured due to machine trouble were excluded. Finally, nine patients were included in the analysis. Using the ERD data detected by RP, two outliers were excluded, and 10 patients were analyzed (Figure 5)” (Lines 249–260).
In addition, "final analysis" in Lines 249–250 has been corrected to "experimental analysis."
Comment 3.
Line 272: In the MSM, patient 1 was excluded the patient was an outlier among the 13 patients included in the experiment – the sentence makes no sense.
Response to Comment 3
Thank you for the comment. We agree with your comment and have deleted this sentence.
Comment 4.
The median onset stroke in this study was 84 days (some patients were 29 days from onset, other 194). The potential of recovery (plasticity) is very different for those patients. Why did you decide to include so divergent group?
Response to Comment 4
Thank you for this comment. This study was conducted in a convalescent hospital and aimed to develop a BCI-robot application for post-stroke patients with severe upper limb paralysis to be used in rehabilitation training. Since we verified that ERD can be detected even in severely paralyzed patients, there was a variation in the time from the onset of stroke among the patients. We analyzed the data by prioritizing the application conditions to patients with severe stroke rather than the number of days after stroke onset. The time since the onset may be a factor that influences ERD detection because it partially defines a patient's likelihood of recovery, which we have added in the Discussion section as follows:
“The time since stroke onset varied among patients, with a median of 84 days, a minimum of 29 days, and a maximum of 194 days. The aim of this study was to verify whether ERD can be detected in patients with severe paralysis of the upper extremities in the recovery phase when they perform MI while watching a movie about arm movements. Patients with stroke experience long-standing deterioration of brain function due to disused plasticity after stroke onset [14]. Although inactivation of the motor cortex was presumed to have occurred in the participants of the present study, ERD was detected in 12 of the 13 patients. In this study, 20 MI sessions were performed as a practice set before analyzing the test set. Our results suggest that MI can excite the motor cortex and detect ERD, even in patients with short or long periods of inactivity due to severe paralysis of the upper extremities.”
(Lines 333–343).
Author Response File: 
Author Response.pdf
Reviewer 2 Report
1. The following works, which contain numerous techniques and methods used in rehabilitation, can be mentioned in the introduction:
§ https://doi.org/10.1016/j.measurement.2023.112826
§ DOI: 10.1016/j.pmrj.2011.05.014
§ Latreche, A., Kelaiaia, R., & Chemori, A. (2023, May). AI-based Human Tracking for Remote Rehabilitation Progress Monitoring. In ICAECE 2023-International Conference on Advances in Electrical and Computer Engineering.
§ https://doi.org/10.14716/ijtech.v8i2.6167
2. There are many symbols and abbreviations. A list (Nomenclature) should be given.
3. The authors do not provide any explicit criticism of the methodology employed in the paper. They only present a detailed description of the methodology used in the study, including the recording of EEG signals during motor imagery, the detection of ERD from EEG using a classifier of MSM and RP, and the measurement of reaction time.
4. The study also used the visual analog scale (VAS) for the subjective evaluation of MI by patients and analyzed the correlation between the MI quality by VAS and the reaction time from EEG playback to ERD detection using Spearman’s rank correlation coefficient. The authors do not provide any subjective interpretation or critique of the methodology used in the study.
5. The authors should conduct a comparative analysis and explain the advantages of the proposed method compared with the existing ones.
Author Response
Reviewer #2
Comments1.
The following works, which contain numerous techniques and methods used in rehabilitation, can be mentioned in the introduction:
- https://doi.org/10.1016/j.measurement.2023.112826
- DOI: 10.1016/j.pmrj.2011.05.014
- Latreche, A., Kelaiaia, R., & Chemori, A. (2023, May). AI-based Human Tracking for Remote Rehabilitation Progress Monitoring. In ICAECE 2023-International Conference on Advances in Electrical and Computer Engineering.
- https://doi.org/10.14716/ijtech.v8i2.6167
Response to Comment 1
We appreciate your useful comments regarding our manuscript and your provision of some references for us to work with. We have revised the Introduction section as follows with references to previous works:
“Exercise-support robots can provide exercise assistance on behalf of the therapist [6,7], and monitoring robots that allow remote therapist-patient interaction allow patients to train under therapist supervision at home [8]. In addition, robots that can encourage independent practice have been developed to enable effective training regardless of location and time [9].” (Lines 43–47)
Comment 2.
There are many symbols and abbreviations. A list (Nomenclature) should be given.
Response to Comment 2
Thank you for your comment. We have added a list of abbreviations (Lines 408–414).
Comment 3.
The authors do not provide any explicit criticism of the methodology employed in the paper. They only present a detailed description of the methodology used in the study, including the recording of EEG signals during motor imagery, the detection of ERD from EEG using a classifier of MSM and RP, and the measurement of reaction time.
Response to Comment 3
Thank you for this comment. As the reviewer stated, what we described in the manuscript was not a critical examination of the methodology itself but a description of the limitations of the procedure. Therefore, we have critically examined the methodology and described further limitations as follows: “Our analyses were conducted in a laboratory using sampled patients' EEG, and the patients did not directly manipulate the DiC. Therefore, this study did not prove that patients could operate the DiC system appropriately. In addition, the time from stroke onset varied among patients, which may have caused individual differences in the quality of MI due to the different degrees of progression of disused plasticity. In our next study, a more limited investigation of the onset period is needed to clarify the operability of the DiC.” Please see lines 391–397.
Comment 4.
The study also used the visual analog scale (VAS) for the subjective evaluation of MI by patients and analyzed the correlation between the MI quality by VAS and the reaction time from EEG playback to ERD detection using Spearman’s rank correlation coefficient. The authors do not provide any subjective interpretation or critique of the methodology used in the study.
Response to Comment 4
Thank you for your comment. In line with your comment, the subjective interpretation and critical consideration of the methodology for correlation between the quality of MI and the reaction time are described in the Discussion section. Please refer to lines 356–365.
“In addition, there was no correlation between the reaction time from ERD detection to the activation of the DiC and the subjective quality of MI. Two types of MI have been reported: visual motor imagery (VMI) and kinesthetic motor imagery (KMI) [22]. A previous study showed correlation between KMI and the ERD magnitude during MI [22]. However, the results of this study suggested that the quality of MI did not affect the time to onset of ERD. This infers that subjective KMI is not related to how quickly corticospinal excitability is transmitted but to how large the change from resting to motor imagery state is. Therefore, it is difficult to improve the quality of MI to reduce ERD detection time, and the control system needs to be adjusted by improving the application to reduce detection time. ”
Comment 5.
The authors should conduct a comparative analysis and explain the advantages of the proposed method compared with the existing ones.
Response to Comment 5
We would like to thank reviewer #2 for the helpful comments. Previous studies have compared the applications of the MSM and RP (ref. [19], Lisi, G.; Rivela, D.; Takai, A.; Morimoto, J. Markov switching model for quick detection of event related desynchronization in EEG. Front Neurosci 2018, 12. DOI: 10.3389/fnins.2018.00024). The MSM has been reported to process faster. One of the purposes of this research was to install the MSM analysis application in the DiC and confirm whether the processing speed was as good as the previous research. Therefore, since the purpose of this study was to confirm that ERD can be detected by MSM and RP algorithms and that the response time of the DiC was faster with the MSM, we decided to conclude the analysis without performing a comparative analysis. We appreciate the reviewers for their understanding.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Thanks for the revision. I don't have any further comments. One minor remark: some suggested references (https://doi.org/10.1016/j.measurement.2023.112826) are not taken into account.
Author Response
Reviewer’s comments and responses
Comments and Suggestions for Authors
Reviewer #2
Comments in 2nd Round
Thanks for the revision. I don't have any further comments. One minor remark: some suggested references (https://doi.org/10.1016/j.measurement.2023.112826) are not taken into account.
Response to Comment
We thank Reviewer #2 for providing us with useful references and insights regarding the revision of our manuscript. After much refinement, we have cited two additional references and adopted the four references suggested by Reviewer #2. We have added the following text:
“Techniques have also been developed to assess joint motion with accuracy comparable to that of humans and evaluate the effectiveness of these robotic exercises [10,11].” (Lines 47−49).
We sincerely appreciate Reviewer #2 for the kind consideration.
Reviewer #2’s Comments In First Round
The following works, which contain numerous techniques and methods used in rehabilitation, can be mentioned in the introduction:
- https://doi.org/10.1016/j.measurement.2023.112826
- DOI: 10.1016/j.pmrj.2011.05.014
- Latreche, A., Kelaiaia, R., & Chemori, A. (2023, May). AI-based Human Tracking for Remote Rehabilitation Progress Monitoring. In ICAECE 2023-International Conference on Advances in Electrical and Computer Engineering.
- https://doi.org/10.14716/ijtech.v8i2.6167
