Name
Title | ||
|---|---|---|
Incorporation Of Inter-Subject Information To Improve The Accuracy Of Subject-Specific P300 Classifiers |
Abstract | ||
|---|---|---|
Although the inter-subject information has been demonstrated to be effective for a rapid calibration of the P300-based brain-computer interface (BCI), it has never been comprehensively tested to find if the incorporation of heterogeneous data could enhance the accuracy. This study aims to improve the subject-specific P300 classifier by adding other subject's data. A classifier calibration strategy, weighted ensemble learning generic information (WELGI), was developed, in which elementary classifiers were constructed by using both the intra-and inter-subject information and then integrated into a strong classifier with a weight assessment. 55 subjects were recruited to spell 20 characters offline using the conventional P300-based BCI, i.e. the P300-speller. Four different metrics, the P300 accuracy and precision, the round accuracy, and the character accuracy, were performed for a comprehensive investigation. The results revealed that the classifier constructed on the training dataset in combination with adding other subject's data was significantly superior to that without the inter-subject information. Therefore, the WELGI is an effective classifier calibration strategy which uses the inter-subject information to improve the accuracy of subject-specific P300 classifiers, and could also be applied to other BCI paradigms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1142/S0129065716500106 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
Brain-computer interface, event-related potential, P300-speller, inter-subject information, classifier calibration | Pattern recognition,Computer science,Brain–computer interface,Datasets as Topic,Artificial intelligence,Accuracy and precision,Classifier (linguistics),Ensemble learning,Machine learning,Calibration | Journal |
Volume | Issue | ISSN |
26 | 3 | 0129-0657 |
Citations | PageRank | References |
9 | 0.52 | 18 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minpeng Xu | 1 | 27 | 17.17 |
| Jing Liu | 2 | 135 | 45.52 |
| Long Chen | 3 | 9 | 1.19 |
| Hongzhi Qi | 4 | 49 | 20.61 |
| Feng He | 5 | 16 | 9.45 |
| Peng Zhou | 6 | 13 | 6.25 |
| Baikun Wan | 7 | 104 | 16.90 |
| Dong Ming | 8 | 105 | 51.47 |