Name
Title | ||
|---|---|---|
Spectral analysis of brain function network for the classification of motor imagery tasks |
Abstract | ||
|---|---|---|
In order to deal with the classification for multi-class motor imagery(MI) tasks, a novel approach was presented in this paper. It is different from classical methods which classified the MI task with time-frequency analysis on EEG signals. It employs the brain function network(BFN) as a new characteristic to describe MI tasks. The BFN enlarges the features with respect to traditional time-frequency methods. Unlike analysis of statistical parameters of network such as average clustering coefficient (C) and the average pathlength (L), the proposed method employed spectral decomposition performing on BFNs, and considered the eigenvalue vector of threshold matrix as features for classification by SVM. Hence, it is speedy enough to meet the requirement of real-time in BCI-based application systems. The result of experiment demonstrates that proposed method can achieve satisfied accuracy of classification on multi-class MI tasks. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/BMEI.2011.6098491 | BMEI |
Keywords | Field | DocType |
bci,eigenvalue vector,neurophysiology,electroencephalography,brain-computer interfaces,spectral analysis,svm,multiclass motor imagery tasks,average pathlength,spectral decomposition,support vector machine,brain function network,classification,brain computer interface,eeg,eigenvalues and eigenfunctions,bci-based application systems,average clustering coefficient,support vector machines,real-time systems,motor imagery,time-frequency analysis,clustering coefficient,correlation matrix,satisfiability,real time,time frequency analysis,correlation,time frequency,eigenvalues,brain computer interfaces,real time systems,feature extraction,matrix decomposition | Statistical parameter,Pattern recognition,Computer science,Brain–computer interface,Support vector machine,Matrix decomposition,Feature extraction,Time–frequency analysis,Artificial intelligence,Clustering coefficient,Motor imagery | Conference |
Volume | Issue | ISBN |
2 | null | 978-1-4244-9351-7 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wanzeng Kong | 1 | 91 | 22.56 |
| Xin-Wei Guo | 2 | 10 | 2.38 |
| Xinxin Zhao | 3 | 123 | 10.91 |
| Daming Wei | 4 | 215 | 44.97 |
| Sanqing Hu | 5 | 452 | 42.72 |
| Guojun Dai | 6 | 472 | 41.96 |
| Giovanni Vecchiato | 7 | 155 | 20.90 |
| fabio babiloni | 8 | 365 | 69.78 |