Name
Abstract | ||
|---|---|---|
We investigate and demonstrate the sparsity of electroencephalography (EEG) signals in the spatial domain by incorporating grid spacing in the area of the head enclosing the brain volume. We exploit this spatial sparsity and propose a new approach for tracking neural activity that is based on compressive particle filtering. Our approach results in reducing the number of EEG channels required to be stored and processed for neural tracking using particle filtering. Simulations using both synthetic and real EEG signals illustrate that the proposed algorithm has tracking performance comparable to existing methods while using only a reduced set of EEG channels. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICASSP.2012.6288661 | ICASSP |
Keywords | Field | DocType |
electroencephalography signals,spatial sparsity,electroencephalography,spatial compressive particle filtering,medical signal processing,compressive sensing,dipole model,eeg signals,filtering theory,eeg,neural activity tracking,grid spacing,multiple particle filter,compressed sensing,mathematical model,vectors | Computer vision,Pattern recognition,Computer science,Particle filter,Neural activity,Communication channel,Artificial intelligence,Atmospheric measurements,Dipole model,Grid,Electroencephalography,Compressed sensing | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4673-0044-5 | 978-1-4673-0044-5 | 1 |
PageRank | References | Authors |
0.43 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lifeng Miao | 1 | 23 | 3.43 |
| Jun Jason Zhang | 2 | 122 | 18.78 |
| Antonia Papandreou-Suppappola | 3 | 234 | 29.88 |
| Chaitali Chakrabarti | 4 | 1978 | 184.17 |