Title :
Tracking non-stationary spectral peak structure in EEG data
Author :
Prerau, M.J. ; Purdon, P.L. ; Eden, Uri T.
Author_Institution :
Dept. of Anesthesia, Critical Care, & Pain Med., Massachusetts Gen. Hosp., Charlestown, MA, USA
Abstract :
We develop a particle filter algorithm to simultaneously estimate and track the instantaneous peak frequency, amplitude, and bandwidth of multiple concurrent non-stationary components of an EEG signal in the time-frequency domain. We use this method to characterize human EEG activity during anesthesia-induced unconsciousness.
Keywords :
electroencephalography; medical signal processing; neurophysiology; particle filtering (numerical methods); signal reconstruction; EEG data; EEG signal; anesthesia-induced unconsciousness; human EEG activity; instantaneous peak frequency; multiple concurrent nonstationary components; particle filter algorithm; simultaneous estimation; time-frequency domain; tracking nonstationary spectral peak structure; Bandwidth; Brain models; Electroencephalography; Frequency estimation; Particle filters; Time-frequency analysis;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
DOI :
10.1109/EMBC.2013.6609525
