Name
Abstract | ||
|---|---|---|
High-speed steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been demonstrated in several recent studies. This study aimed to investigate some issues regarding feasibility of learning to control an SSVEP-based BCI speller in naive subjects. An experiment with new BCI users was designed to answer the following questions: (1) How many people can use the SSVEP-BCI speller? (2) How much time is required to train the user? (3) Does continuous system use lead to user fatigue and deteriorated BCI performance? The experiment consisted of three tasks including a 40-class BCI spelling task, a psychomotor vigilance test (PVT) task, and a test of sleepiness scale. Subjects' reaction time (RT) in the PVT task and the fatigue rank in the sleepiness scale test were used as objective and subjective parameters to evaluate subjects' alertness level. Among 11 naive subjects, 10 of them fulfilled the 9-block experiment. Four of them showed clear learning effects (i.e., an increasing trend of classification accuracy and information transfer rate (ITR)) over time. The remaining subjects showed stable BCI performance during the whole experiment. The results of RT and fatigue rank showed a gradually increasing trend, which is not significant across blocks. In summary, the results of this study suggest that controlling an SSVEP-based BCI speller is in general feasible to learn by naive subjects after a short training procedure, showing no clear performance deterioration related to fatigue. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8037227 | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Learning effect,Psychomotor learning,Information transfer,Computer science,Brain–computer interface,Speech recognition,Vigilance (psychology),Spelling,Alertness | Conference | 2017 |
ISSN | Citations | PageRank |
1094-687X | 0 | 0.34 |
References | Authors | |
1 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhihua Tang | 1 | 0 | 0.34 |
| Yijun Wang | 2 | 308 | 46.68 |
| Guoya Dong | 3 | 1 | 2.19 |
| Weihua Pei | 4 | 64 | 13.18 |
| Hongda Chen | 5 | 99 | 20.06 |