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

Abstract

BackgroundRhythmic 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.MethodsIn 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.ResultsOur 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.ConclusionModeling 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.

Drowsiness detection for single channel EEG by DWT best m-term approximation

“…Higher rates were reported by Lawhern et al (2013). Hit rates higher than 94% were obtained by applying a sequential discounted autoregressive algorithm on a digital filtered EEG for detecting alpha spindles.…”

“…EEG signals were preprocessed with an independent component analysis with reference (ICA-R) algorithm for electrooculography (EOG) interference removal. Another interesting set of results was presented by Lawhern et al (2013): their detection strategy relied on the identification of alpha spindles employing discounted autoregressive (DAR) modeling.…”

“…The technique can be attractive for implementing fatigue detection on low-power devices because DWT kernels require fewer execution steps than FFT algorithms (complexities for transforming 1D signals with N points are respectively in the order of N and N.log 2 (N) steps (Beylkin et al, 1991)). Additionally, simple threshold techniques are employed instead of classification strategies based on heavy matrix operations (Lawhern et al, 2013;Liang et al, 2006;Lin et al, 2010). Another advantage of the proposed technique is the fact that no previous artifact rejection is necessary, since the thresholded wavelet representation of the EEG signal already works as a type of filtering process, removing low frequencies from the analyzed signal.…”

EEG Beta Wave Trains Are Not the Second Harmonic of Mu Wave Trains in Parkinson’s Disease Patients

“…The idea of our method of analyzing EEG is in that we consider EEG signal as a composition of so-called wave trains. In contract to papers devoted to detecting wave trains of one or two specific types, such as alpha spindles [1] and sleep spindles [2,3,4,5,6,7], we analyze any kind of wave trains in a wide frequency area. The developed method differs from analogous method for detailed analysis of time-frequency dynamics of EEG [8] in that the statistical analysis of samples of wave trains and a new method for visualizing the results of the analysis are proposed.…”

Development of a Method of Analysis of Eeg Wave Packets in Early Stages of Parkinson's Disease

“…Alpha spindles (sleep spindles) and beta spindles are the best known examples of the wave packets in EEG; several methods based on Fourier spectra, wavelets, autoregressive models, adaptive filtering, etc. have been developed for detecting and analysing these EEG patterns (see surveys in [1,2,3]). The idea of our method of EEG analysis is in that we detect and analyse the wave packets in a wide frequency band including theta, alpha, beta, and gamma EEG.…”