Name
Abstract | ||
|---|---|---|
Brain-computer interface (BCI) has been developed so that people can control computers or machines using their brain activity. Thus, BCI provides people who cannot move their bodies with new communication tools. The P300 speller is one of the applications used most commonly among the various types of BCI. Compared to other BCIs, the P300 speller achieves relatively high accuracy, but there remains room for improvement, such as reduction in calibration time and speeding up the speller by modulating the number of repetitions needed to enter a character. Hence, seeking correlates of the P300 speller performance may help improve the application. In this experiment, we investigated the correlation between a rapid serial visual presentation (RSVP) task and P300 speller performance to determine whether such a task is useful in predicting P300 speller performance. We found statistically significant correlations between the RSVP task ERP (P300) features and the P300 speller offline accuracy. Based on this observation, we expect that P300 speller performance can be predicted using a relatively simple cognitive task. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SMC.2018.00201 | 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) |
Keywords | Field | DocType |
BCI, P300 speller, RSVP, performance prediction | Computer science,Brain–computer interface,Brain activity and meditation,Correlation,Artificial intelligence,Cognition,Performance prediction,Machine learning,Rapid serial visual presentation | Conference |
ISSN | Citations | PageRank |
1062-922X | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyungho Won | 1 | 0 | 1.69 |
| Moonyoung Kwon | 2 | 0 | 2.37 |
| Sunghan Lee | 3 | 0 | 0.34 |
| Sehyeon Jang | 4 | 0 | 0.68 |
| Jong-Min Lee | 5 | 111 | 23.54 |
| Minkyu Ahn | 6 | 47 | 4.09 |
| Sung C. Jun | 7 | 111 | 14.49 |