Name
Abstract | ||
|---|---|---|
Motor function assessment is crucial in quantifying motor recovery following stroke. In the rehabilitation field, motor function is usually assessed using questionnaire-based assessments, which are not completely objective and require prior training for the examiners. Some research groups have reported that electroencephalography (EEG) data have the potential to be a good indicator of motor function. However, those motor function scores based on EEG data were not evaluated in a longitudinal paradigm. The ability of the motor function scores from EEG data to track the motor function changes in long-term clinical applications is still unclear. In order to investigate the feasibility of using EEG to score motor function in a longitudinal paradigm, a convolutional neural network (CNN) EEG model and a residual neural network (ResNet) EEG model were previously generated to translate EEG data into motor function scores. To validate applications in monitoring rehabilitation following stroke, the pre-established models were evaluated using an initial small sample of individuals in an active 14-week rehabilitation program. Longitudinal performances of CNN and ResNet were evaluated through comparison with standard Fugl-Meyer Assessment (FMA) scores of upper extremity collected in the assessment sessions. The results showed good accuracy and robustness with both proposed networks (average difference: 1.22 points for CNN, 1.03 points for ResNet), providing preliminary evidence for the proposed method in objective evaluation of motor function of upper extremity in long-term clinical applications. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20195487 | SENSORS |
Keywords | DocType | Volume |
EEG,motor function,neural networks | Journal | 20 |
Issue | ISSN | Citations |
19 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Zhang | 1 | 0 | 0.34 |
| Ryan D'Arcy | 2 | 0 | 0.34 |
| Long Chen | 3 | 0 | 3.04 |
| Minpeng Xu | 4 | 27 | 17.17 |
| Dong Ming | 5 | 0 | 0.34 |
| Carlo Menon | 6 | 0 | 0.34 |