Name
Title | ||
|---|---|---|
Nonlinear Markov process amplitude EEG model for nonlinear coupling interaction of spontaneous EEG. |
Abstract | ||
|---|---|---|
To develop an appropriate model for representing spontaneous electroencephalography (EEG) is an important and necessary work in the field of neuroscience. The Markov process amplitude (MPA) EEG model has been proposed in our previous work for representing the features of the EEG in terms of a few parameters. However, being a linear model, the linear MPA EEG model cannot perfectly describe the spon... |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/10.867917 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
Electroencephalography,Brain modeling,Couplings,Markov processes,Frequency domain analysis,Control engineering,Rhythm,Neuroscience,Displays,Power generation | Signal processing,Nonlinear system,Markov process,Computer science,Spectral density,Artificial intelligence,Electroencephalography,Statistical physics,Time domain,Frequency domain,Computer vision,Linear model,Speech recognition | Journal |
Volume | Issue | ISSN |
47 | 9 | 0018-9294 |
Citations | PageRank | References |
8 | 0.93 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ou Bai | 1 | 50 | 14.24 |
| M. Nakamura | 2 | 149 | 30.71 |
| A Ikeda | 3 | 8 | 1.61 |
| H Shibasaki | 4 | 11 | 2.16 |