Name
Abstract | ||
|---|---|---|
For the past few years, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have gotten tremendous progress and attracted increasing attention. To broaden the application of BCIs, researchers have focused on the increasement of the BCI instruction number in recent years. However, with a large number of instructions, the BCI calibration time will be too long to be accepted in practical usage. This study proposed a new coding method based on multifocal steady-state visual evoked potentials (mfSSVEPs), in which 16 targets were binary coded by 4 frequencies. Notably, the training data needed for calibration corresponded to only five out of the sixteen targets. Five volunteers were recruited to test this paradigm. Task-related component analysis combined with a probabilistic model were employed for target recognition. As a result, the accuracy could reach as high as 93.1% with Is-length data. The highest information transfer rate (ITR) was 101.1 bits/min with an average of 73.9 bits/min. The results indicate that this new paradigm is promising to encode a large BCI instruction set with less trainings. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857450 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,Information transfer,Instruction set,Computer science,Frequency-shift keying,Signal-to-noise ratio,Brain–computer interface,Coding (social sciences),Speech recognition,Artificial intelligence,Statistical model,Electroencephalography | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiabei Tang | 1 | 1 | 3.42 |
| Minpeng Xu | 2 | 27 | 17.17 |
| Zheng Liu | 3 | 4 | 0.79 |
| Jiayuan Meng | 4 | 0 | 1.35 |
| Shanguang Chen | 5 | 0 | 0.68 |
| Dong Ming | 6 | 105 | 51.47 |