EEG analysis is exploiting mathematical signal analysis methods and computer technology to extract information from electroencephalography (EEG) signals. The targets of EEG analysis are to help researchers gain a better understanding of the brain; assist physicians in diagnosis and treatment choices; and to boost brain-computer interface (BCI) technology. There are many ways to roughly categorize EEG analysis methods. If a mathematical model is exploited to fit the sampled EEG signals, the method can be categorized as parametric, otherwise, it is a non-parametric method. Traditionally, most EEG analysis methods fall into four categories: time domain, frequency domain, time-frequency domain, and nonlinear methods. There are also later methods including deep neural networks (DNNs).
Property | Value |
---|---|
dbo:abstract |
|
dbo:wikiPageID |
|
dbo:wikiPageLength |
|
dbo:wikiPageRevisionID |
|
dbo:wikiPageWikiLink |
|
dbp:wikiPageUsesTemplate | |
dct:subject | |
rdfs:comment |
|
rdfs:label |
|
owl:sameAs | |
prov:wasDerivedFrom | |
foaf:isPrimaryTopicOf | |
is dbo:wikiPageWikiLink of | |
is foaf:primaryTopic of |