A Review of Most Current Feature Extraction Methods for EEG Signal Processing

Safari, MJ; Kordi, A; Oladazimi, M; Shiman, F; Molaei-Vaneghi, F

doi:10.1115/1.859940.paper12

Datensatz

DATENSATZ AKTIONENEXPORT

Zur Ablage hinzufügen

Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben

Konferenzbeitrag

A Review of Most Current Feature Extraction Methods for EEG Signal Processing

MPG-Autoren

Es sind keine MPG-Autoren in der Publikation vorhanden

Externe Ressourcen

http://ebooks.asmedigitalcollection.asme.org/content.aspx?bookid=406&sectionid=38787689
(Verlagsversion)

Volltexte (beschränkter Zugriff)

Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.

Volltexte (frei zugänglich)

Es sind keine frei zugänglichen Volltexte in PuRe verfügbar

Ergänzendes Material (frei zugänglich)

Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar

Zitation

Safari, M., Kordi, A., Oladazimi, M., Shiman, F., & Molaei-Vaneghi, F. (2012). A Review of Most Current Feature Extraction Methods for EEG Signal Processing. In J. Zhou (Ed.), 4th International Conference on Computer and Automation Engineering (ICCAE 2012) (pp. 77-84). New York, NY, USA: ASME Press.

Zitierlink: https://hdl.handle.net/21.11116/0000-0001-8F65-D

Zusammenfassung

For investigation of the brain signal, using the appropriate method for feature extraction is essential. During these years different methods have been offered for processing the EEG signals such as frequency domain, time domain and time-frequency domain. The methods used in this study are Wavelet transforms, CSP, Fourier transform, Eigenvector and EMD. The main purpose of this research is to investigate the advantages and disadvantages of these methods and provide extensive comparison between them. This study shows that between these methods EMD is the best method for feature extraction of the EEG signal because it is more adaptive to non-stationary signal.