Detecting alpha spindle events in EEG time series using adaptive autoregressive models.

ABSTRACT:

Background

Rhythmic oscillatory activity is widely observed during a variety of subject behaviors and is believed to play a central role in information processing and control. A classic example of rhythmic activity is alpha spindles, which consist of short (0.5-2 s) bursts of high frequency alpha activity. Recent research has shown that alpha spindles in the parietal/occipital area are statistically related to fatigue and drowsiness. These spindles constitute sharp changes in the underlying statistical properties of the signal. Our hypothesis is that change point detection models can be used to identify the onset and duration of spindles in EEG. In this work we develop an algorithm that accurately identifies sudden bursts of narrowband oscillatory activity in EEG using techniques d

SUBMITTER: Lawhern V 

PROVIDER: S-EPMC3848457 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature