DocumentCode :
2045275
Title :
Extraction of feature information in EEG signal by virtual EEG instrument with the functions of time-frequency analysis
Author :
Qin Shuren ; Ji Zhong
Author_Institution :
Test Center, Chongqing Univ., Chongqing, China
fYear :
2009
fDate :
16-18 Sept. 2009
Firstpage :
7
Lastpage :
11
Abstract :
As a complex non-stationary signal, to extract the feature information in EEG signals efficiently, the application of various time-frequency analysis methods for EEG signals analysis have been discussed in theory and gotten many study fruits. However, it is reported little in literature that how to make these theoretical research productions be useful algorithms in practice and integrate them into EEG detection and analysis instrument. Based on virtual instrument technology, the time-frequency analysis methods on the detection of EEG signals have been further discussed from theory, then the concrete algorithms of the time-frequency analysis methods used for the extraction of EEG feature rhythms have been established and integrated into the virtual EEG instrument. By this way, the time-frequency analysis methods to be used to detect and extract the feature information in EEG signals automatically can be realized in clinical.
Keywords :
data acquisition; electroencephalography; feature extraction; medical signal processing; time-frequency analysis; virtual instrumentation; EEG analysis instrument; EEG detection instrument; EEG signal feature extraction; complex nonstationary signal; time-frequency analysis algorithms; virtual EEG instrument; Algorithm design and analysis; Concrete; Data mining; Electroencephalography; Feature extraction; Instruments; Production; Signal analysis; Signal detection; Time frequency analysis; EEG; Feature Information; Instrument; Time-Frequency Analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing and Analysis, 2009. ISPA 2009. Proceedings of 6th International Symposium on
Conference_Location :
Salzburg
ISSN :
1845-5921
Print_ISBN :
978-953-184-135-1
Type :
conf
DOI :
10.1109/ISPA.2009.5297794
Filename :
5297794
Link To Document :
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