Name
Title | ||
|---|---|---|
Performance Evaluation of Walking Imagery Training Based on Virtual Environment in Brain-Computer Interfaces |
Abstract | ||
|---|---|---|
Motor imagery (MI) based on brain computer interfaces (BCIs) have been widely applied for upper limb motor rehabilitation. Due to the fact that a large number of disabled people need to restore or improve walking ability, it is also important to investigate the use of MI-based BCIs for lower limb motor rehabilitation. The brain activity of lower limb MI is more difficult to detect because of low reliability. The purpose of this study is to find a suitable paradigm of walking imagery to achieve better training effect and ensure reliable brain activity. We developed the text-based paradigm and the virtual environment (VE)-based paradigm, and evaluated their performance on identifying walking imagery from idle state.The experimental results provide evidences that the VE-based paradigm could improve the average classification accuracy. This paradigm would induce EEG patterns that make them easier for single-trial detection of walking imagery. This study has the potential to improve the reliability and robustness of walking imagery based BCIs. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ISM.2017.15 | 2017 IEEE International Symposium on Multimedia (ISM) |
Keywords | Field | DocType |
brain activity,EEG patterns,text-based paradigm,average classification accuracy,virtual environment based paradigm,lower limb motor rehabilitation,walking ability,upper limb motor rehabilitation,motor imagery,brain-computer interfaces,imagery training,walking imagery | Computer vision,Virtual machine,Computer science,Visualization,Brain–computer interface,Brain activity and meditation,Robustness (computer science),Training effect,Human–computer interaction,Artificial intelligence,Electroencephalography,Motor imagery | Conference |
ISBN | Citations | PageRank |
978-1-5386-2938-3 | 0 | 0.34 |
References | Authors | |
5 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaolu Liu | 1 | 33 | 3.71 |
| Shuang Liang | 2 | 43 | 6.12 |
| Wenlong Hang | 3 | 5 | 2.12 |
| Baiying Lei | 4 | 271 | 34.50 |
| Qiong Wang | 5 | 30 | 15.18 |
| Jing Qin | 6 | 1109 | 95.43 |
| Kup-Sze Choi | 7 | 526 | 47.41 |