Name
Abstract | ||
|---|---|---|
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize high-speed communication between the human brain and the external environment. Recently, multiple access (MA) methods in telecommunications have been introduced into the system design of BCIs and showed their potential in improving BCI performance. This study investigated the feasibility of hybrid frequency and phase coding methods in multi-target SSVEP-based BCIs. Specifically, this study compared two hybrid target-coding strategies: (1) mixed frequency and phase coding, and (2) joint frequency and phase coding. In a simulated online BCI experiment using a 40-target BCI speller, BCI performance for both coding approaches were tested with a group of six subjects. At a spelling speed of 40 characters per minute (1.5 seconds per character), both approaches obtained high information transfer rates (ITR) (mixed coding: 172.37±28.67 bits/min, joint coding: 170.94±28.32 bits/min) across subjects. There was no statistically significant difference between the two approaches (p>0.05). These results suggest that the hybrid frequency and phase coding methods are highly efficient for multi-target coding in SSVEP BCIs with a large number of classes, providing a practical solution to implement a high-speed BCI speller. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6944499 | EMBC |
Keywords | Field | DocType |
online bci experiment,high-speed bci speller,steady-state visual evoked potential,high-speed ssvep-based bci speller,brain-computer interfaces,visual evoked potentials,frequency coding methods,hybrid target-coding strategy,phase coding methods,phase coding | Computer vision,Computer science,Brain–computer interface,Speech recognition,Phase coding,Artificial intelligence | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaogang Chen | 1 | 1 | 1.09 |
| Yijun Wang | 2 | 308 | 46.68 |
| Masaki Nakanishi | 3 | 3 | 1.39 |
| Tzyy-Ping Jung | 4 | 144 | 23.93 |
| Xiaorong Gao | 5 | 0 | 0.34 |