Name
Abstract | ||
|---|---|---|
The subjects' performance in using a brain-computer interface (BCI) system controlled by motor imagery (MI) varies considerably. Poor subjects' performance, known as BCI deficiency, can be due to the subjects' inability to modulate their sensorimotor rhythms (SMRs). In this work, we investigated the feasibility of improving the BCI performance through neurofeedback (NF) training of the resting state alpha rhythm (8-13 Hz). Thirteen healthy subjects were recruited and randomly assigned to the experimental or the control group. The experimental group participated in a MI-BCI session, followed by 12 NF sessions, and a final MI-BCI sessions. The control group performed a MI-BCI session followed by a final MI-BCI session. The results showed that the performances of the experimental group after 12 sessions of NF significantly improved upon the initial MI-BCI performance (p=0.02) but not the control group (p=0.14). Moreover, the resting state alpha of the experimental group significantly improved after 12 sessions of NF (p=0.04). In conclusion, the proposed approach is promising to address BCI deficiency. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/NER.2015.7146549 | 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER) |
Keywords | Field | DocType |
MI-BCI performance,BCI deficiency,brain-computer interface system,motor imagery,sensorimotor rhythms,neurofeedback training,alpha rhythm,control group,resting state alpha | Neuroscience,Brain–computer interface,Resting state fMRI,Psychology,Artificial intelligence,Physical medicine and rehabilitation,Rhythm,Machine learning,Neurofeedback,Motor imagery | Conference |
ISSN | Citations | PageRank |
1948-3546 | 1 | 0.35 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Atieh Bamdadian | 1 | 1 | 0.69 |
| Cuntai Guan | 2 | 1298 | 124.69 |
| Kai Keng Ang | 3 | 804 | 64.19 |
| Jian-Xin Xu | 4 | 324 | 37.81 |