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Open Access Highly Accessed Methodology article

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

Vernon Lawhern12*, Scott Kerick2 and Kay A Robbins1

Author affiliations

1 Department of Computer Science, University of Texas at San Antonio, San Antonio, TX 78249, USA

2 Human Research and Engineering Directorate, US Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD 21005, USA

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Citation and License

BMC Neuroscience 2013, 14:101  doi:10.1186/1471-2202-14-101

Published: 18 September 2013

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 derived from change point analysis. Our motivating example is detection of alpha spindles in the parietal/occipital areas of the brain. Our goal is to develop an algorithm that can be applied to any type of rhythmic oscillatory activity of interest for accurate online detection.

Methods

In this work we propose modeling the alpha band EEG time series using discounted autoregressive (DAR) modeling. The DAR model uses a discounting rate to weigh points measured further in the past less heavily than points more recently observed. This model is used together with predictive loss scoring to identify periods of EEG data that are statistically significant.

Results

Our algorithm accurately captures changes in the statistical properties of the alpha frequency band. These statistical changes are highly correlated with alpha spindle occurrences and form a reliable measure for detecting alpha spindles in EEG. We achieve approximately 95% accuracy in detecting alpha spindles, with timing precision to within approximately 150 ms, for two datasets from an experiment of prolonged simulated driving, as well as in simulated EEG. Sensitivity and specificity values are above 0.9, and in many cases are above 0.95, for our analysis.

Conclusion

Modeling the alpha band EEG using discounted AR models provides an efficient method for detecting oscillatory alpha activity in EEG. The method is based on statistical principles and can generally be applied to detect rhythmic activity in any frequency band or brain region.

Keywords:
Alpha spindle; Adaptive autoregressive model; Classification; Electroencephalography; Time series; Fatigue; Change point detection