Name
Abstract | ||
|---|---|---|
This study proposes a novel hybrid brain-computer interface (BCI) approach for increasing the spelling speed. In this approach, the P300 and steady-state visually evoked potential (SSVEP) detection mechanisms are devised and integrated so that the two brain signals can be used for spelling simultaneously. Specifically, the target item is identified by 2-D coordinates that are realized by the two brain patterns. The subarea/location and row/column speedy spelling paradigms were designed based on this approach. The results obtained for 14 healthy subjects demonstrate that the average online practical information transfer rate, including the time of break between selections and error correcting, achieved using our approach was 53.06 bits/min. The pilot studies suggest that our BCI approach could achieve higher spelling speed compared with the conventional P300 and SSVEP spellers. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TBME.2013.2281976 | IEEE Trans. Biomed. Engineering |
Keywords | Field | DocType |
ssvep detection mechanisms,brain patterns,ssvep spellers,medical signal detection,p300 spellers,brain–computer interface (bci),brain-computer interfaces,row-column speedy spelling paradigms,speedy hybrid bci spelling approach,medical signal processing,p300 potential,visual evoked potentials,bci approach,brain-computer interface (bci),2-d coordinates,online practical information transfer rate,steady-state visually evoked potential (ssvep),brain,electroencephalogram (eeg),subarea-location speedy spelling paradigms,hybrid brain-computer interface,steady-state visually evoked potential detection,brain signals,hybrid bci,brain computer interfaces | Information transfer,Computer science,Brain–computer interface,Speech recognition,Spelling | Journal |
Volume | Issue | ISSN |
61 | 2 | 1558-2531 |
Citations | PageRank | References |
21 | 1.46 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erwei Yin | 1 | 110 | 9.12 |
| Zongtan Zhou | 2 | 412 | 33.89 |
| Jun Jiang | 3 | 60 | 5.05 |
| Fanglin Chen | 4 | 95 | 7.96 |
| Yadong Liu | 5 | 105 | 14.04 |
| Dewen Hu | 6 | 1290 | 101.20 |