Automated Author ProfileKoblar, Simon
University of Adelaide
Koblar, Simon
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.7 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The shared data relates to a study to assess the effectiveness of an innovative BCI therapy, in a cohort of chronic stroke survivors for hand movement recovery. We hypothesized that 18 sessions of our personalised neurofeedback training would lead to a clinically significant reduction in post-stroke arm and hand impairment, with effects persisting for at least four weeks.ResultsOur study recruited chronic stroke survivors from South Australia during 2020–2021, focusing on individuals with stable post-stroke conditions and impaired motor capabilities specifically of the hand. Participants underwent personalized neurofeedback training, which targeted specific EEG channels and frequency bands tailored to their neurophysiological profiles. Training sessions were conducted three times weekly over six weeks, using a customized EEG cap and feedback system to engage motor imagery and relaxation phases, which were adjusted based on the individual's performance and reaction times. Outcome measures, including motor function and sensory feedback performance, were assessed at baseline, immediately post-intervention, and during a 4–6 weeks follow-up to evaluate the lasting impacts of the training. A more detailed explanation of the study design may be seen in the Materials and Methods Section.
ParticipantsFor this study, we screened 25 prospective participants to obtain a sample of 12 stroke patients; 13 candidates did not meet the inclusion/exclusion criteria. The dataset includes the results of the 12 participants included in the trial.* Performance measures*Behavioural changes were monitored using Fugl-Meyer upper extremity (FMA-UE) motor assessment as the primary outcome measure. We also measured the ARAT, the reaction time of the affected and intact hands, unilateral neglect, spasticity, grip and pinch strength of the affected hand, goal attainment scale and Fugl-Meyer upper extremity sensation as our secondary assessment tools. Note that the last four secondary tests were added after the first cohort of participants underwent the study and reported gaining outcomes in their sensory functions and hand strength. As a result, we added Fugl-Meyer upper extremity sensation, grip strength, pinch strength, and goal attainment scale (GAS) tools.
Authors
- Darvishi, Sam ;
- Datta Gupta, Anupam ;
- Hamilton-Bruce, Anne ;
- Koblar, Simon ;
- Baumert, Mathias ;
- Abbott, Derek
The shared data relates to a study to assess the effectiveness of an innovative BCI therapy, in a cohort of chronic stroke survivors for hand movement recovery. We hypothesized that 18 sessions of our personalised neurofeedback training would lead to a clinically significant reduction in post-stroke arm and hand impairment, with effects persisting for at least four weeks.ResultsOur study recruited chronic stroke survivors from South Australia during 2020–2021, focusing on individuals with stable post-stroke conditions and impaired motor capabilities specifically of the hand. Participants underwent personalized neurofeedback training, which targeted specific EEG channels and frequency bands tailored to their neurophysiological profiles. Training sessions were conducted three times weekly over six weeks, using a customized EEG cap and feedback system to engage motor imagery and relaxation phases, which were adjusted based on the individual's performance and reaction times. Outcome measures, including motor function and sensory feedback performance, were assessed at baseline, immediately post-intervention, and during a 4–6 weeks follow-up to evaluate the lasting impacts of the training. A more detailed explanation of the study design may be seen in the Materials and Methods Section.
ParticipantsFor this study, we screened 25 prospective participants to obtain a sample of 12 stroke patients; 13 candidates did not meet the inclusion/exclusion criteria. The dataset includes the results of the 12 participants included in the trial.* Performance measures*Behavioural changes were monitored using Fugl-Meyer upper extremity (FMA-UE) motor assessment as the primary outcome measure. We also measured the ARAT, the reaction time of the affected and intact hands, unilateral neglect, spasticity, grip and pinch strength of the affected hand, goal attainment scale and Fugl-Meyer upper extremity sensation as our secondary assessment tools. Note that the last four secondary tests were added after the first cohort of participants underwent the study and reported gaining outcomes in their sensory functions and hand strength. As a result, we added Fugl-Meyer upper extremity sensation, grip strength, pinch strength, and goal attainment scale (GAS) tools.
Authors
- Darvishi, Sam ;
- Datta Gupta, Anupam ;
- Hamilton-Bruce, Anne ;
- Koblar, Simon ;
- Baumert, Mathias ;
- Abbott, Derek