Name
Abstract | ||
|---|---|---|
A four-class brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEPs) was developed by presenting phase-coded 60Hz stimulations on a 240Hz LCD monitor. The task-related component analysis (TRCA) algorithm was used to detect SSVEPs with individual training data. In the BCI experiment with 10 subjects, the system achieved high classification accuracy of 94.50 +/- 6.70% and 92.71 +/- 7.56% in offline and online BCI experiments, resulting in information transfer rates (ITR) of 19.95 +/- 4.36 and 18.81 +/- 4.74 bpm, respectively. The behavioral tests on visual comfortableness and perception of flickering reveal that the proposed BCI system is very comfortable to use without any perception of flicker. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857326 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,Flicker,Information transfer,Visualization,Computer science,Brain–computer interface,Refresh rate,Artificial intelligence,Statistical classification,Perception,Electroencephalography | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiang Lu | 1 | 10 | 3.70 |
| Yijun Wang | 2 | 308 | 46.68 |
| Weihua Pei | 3 | 64 | 13.18 |
| Hongda Chen | 4 | 99 | 20.06 |