Paper Info
Title
Inter-subject information contributes to the ERP classification in the P300 speller
Abstract
This study aims to investigate whether the inter-subject information is beneficial to the event-related potential (ERP) classification in the P300-speller. To this end, a classification strategy of weighted ensemble learning generic information (WELGI) was developed, in which the base classifiers constructed by combining both intra- and inter-subject information were integrated into a strong classifier with weight assessments. To verify the algorithm's validity, 55 subjects were recruited to spell 20 characters offline by using the conventional P300-speller paradigm, and the ERP accuracy and precision were investigated. Compared with the traditional classification strategy only using the intra-subject information, the WELGI could achieve significantly higher ERP accuracy and precision. It was demonstrated that the inter-subject information was beneficial to the ERP classification in the P300-speller.
Year
DOI
Venue
2015
10.1109/NER.2015.7146596
Neural Engineering
Keywords
Field
DocType
bioelectric potentials,biomedical measurement,brain-computer interfaces,classification,data integration,feature extraction,learning (artificial intelligence),medical signal processing,signal classification,ERP accuracy,ERP classification,ERP precision,P300 speller,WELGI classification,base classifier,conventional P300-speller paradigm,event-related potential classification,inter-subject information integration,intra-subject information integration,offline character spelling,weight assessment,weighted ensemble learning generic information
Computer science,Speech recognition,Artificial intelligence,Spell,Accuracy and precision,Classifier (linguistics),Ensemble learning,Machine learning
Conference
ISSN
Citations 
PageRank 
1948-3546
2
0.45
References 
Authors
8
4
Name
Order
Citations
PageRank
Minpeng Xu12717.17
Jing Liu213545.52
Long Chen38812.45
Hongzhi Qi44920.61