This paper presents an algorithm based on canonical variates transformation (CVT) and distance based discriminant analysis (DBDA) combined with a mental tasks transitions detector (MTTD) to classify spontaneous mental activities in order to operate a brain-computer interface working under an asynchronous protocol. The algorithm won the BCI Competition III--Data Set V: Multiclass Problem, Continuous EEG--achieving an averaged classification accuracy over three subjects of 68.65% (79.60, 70.31 and 56.02%, respectively) in a three-class problem.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s11517-007-0197-7 | DOI Listing |
Publication Analysis
Top Keywords
mental tasks
8
tasks transitions
8
spontaneous mental
8
mental
4
transitions detection
4
detection improve
4
improve spontaneous
4
mental activity
4
activity classification
4
classification paper
4
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!