Adaptive tracking of EEG oscillations
Introduction
Oscillatory phenomena have been the focus of increasing interest in neuroscience research. Neuronal oscillations have been proposed as a key mechanism for the large-scale integration of cognitive processes through which top-down internal states influence stimulus processing (Engel et al., 2001, Varela et al., 2001). Several models have been developed, with oscillations either serving as a binding mechanism bringing together different perceptions into a unified representation (Singer and Gray, 1995, Engel and Singer, 2001) or as a dynamic substrate for neuronal communication achieved through the coherence between brain areas (Fries, 2005). Also a more precise observation of specific oscillatory parameters can shed light on even more detailed brain processes. For instance, the ongoing oscillatory state of the brain before a given stimulus has been shown to provide valuable information about the subsequent behavioral responses in both motor and sensory tasks (Linkenkaer-Hansen et al., 2004, Womelsdorf et al., 2006). Additionally, the phase of neuronal oscillations was successfully linked to activity of single neurons (Jacobs et al., 2007). Finally, increasing evidence indicates that responses within classical neuronal frequency bands likely interact with each other through coupling mechanisms that remain to be identified (Jensen and Colgin, 2007). In this framework, cross-frequency couplings could provide a unifying mechanism for the intermingled neuronal oscillations acting at different temporal and spatial scales (Von Stein and Sarnthein, 2000), and recent studies tend to verify the existence, and the possible importance of cross-frequency couplings, during a variety of motor, sensory and cognitive tasks (Canolty et al., 2006, Lakatos et al., 2007, Demiralp et al., 2007).
Taken together, these findings raise the need for efficient methods for accurate estimation of oscillatory information such as phase, frequency and amplitude from raw signals. A well-known method widely used to get such spectral information is the Hilbert transform and its analytic signal representation (Gabor, 1946). However, although many studies have successfully identified and described phase synchronizations by applying this method to wide-band neuronal signals, it has been shown that proper estimation of oscillatory parameters can be performed only on narrow-band signals (Nho and Loughlin, 1999, Chavez et al., 2006). Moreover, subsequent synchronization measures such as the Phase Locking Value (Tass et al., 1998) are reliable only when applied to narrow-band signals (Celka, 2007). Therefore, band-pass filtering was applied to neuronal signals as a pre-processing step, in order to split the raw signals into narrow-band oscillations of different frequencies. Although this filter bank approach can lead to a more reliable analysis of oscillatory interactions (Canolty et al., 2006), a major drawback of such pre-processing should be mentioned. Because the cut-off frequencies of each band-pass filter must be pre-defined and remain constant during the whole analysis window, physiologically misleading outputs could be produced by the filters, in the case of a frequency component crossing the cut-off frequency limit of a filter. In such situations, it would be preferable to follow an oscillatory component in a continuous manner, without constraining the spectral content to fixed limits. This remark emphasizes the need for adaptive methods able to track narrow-band oscillations over time.
Recently, we proposed a novel method for adaptively tracking multiple oscillations in single-trial EEG signals (Uldry et al., 2009). In this article, we describe the tracking abilities of our algorithm for the estimation of single or multiple frequencies in both synthetic and EEG signals. The physiological relevance of well-known synchronization measures can be assessed using the temporal outputs of our method. Importantly, our previous publication on this tracking scheme is extended in order to clearly illustrate its capabilities for adaptive frequency estimation and its advantages over more traditional approaches for measuring cross-frequency couplings. In Section 2, we present the basics of our algorithm as well as its multi-frequency and multi-signal extensions, and we illustrate its performance on synthetic signals. In Section 3, we present the results of our method on real EEG single-trial signals in terms of adaptive frequency tracking, and demonstrate the benefit of applying common synchronization measures on the temporal outputs of our filters, compared to current methods.
Section snippets
Methods
The oscillation tracking methods are presented within the complex-valued signal framework. This approach simplifies several aspects of the computations. Especially, the filters are shorter (only one pole is needed for a complex band-pass filter, whereas two poles are required for a real band-pass filter). Of course, the signals of interest are real-valued in practice. But with the Hilbert transform one obtains the so-called analytic representation, whose real part is the original signal itself.
Results
The data we present have been taken from a previously published study examining the spatio-temporal mechanisms of illusory contour perception with broad-band event-related potentials (Murray et al., 2002). This paradigm was chosen here as it represents a typical situation where time–frequency analyses based on wavelet decomposition have been extensively applied and have led to propositions regarding the role of gamma oscillations as a binding mechanism in human cortex (Tallon-Baudry and
Discussion
Oscillatory phenomena have gained increasing importance in the field of neuroscience, particularly because improvements in analysis methods have revealed how oscillatory activity is both a highly efficient and also information-rich signal. One paradigmatic shift in the conceptualization of oscillatory activity has been to consider not only changes within a particular frequency band, but also the interactions and synchronizations between frequencies of brain activity that are in turn thought to
Acknowledgments
We thank the anonymous reviewers for helpful and constructive comments. This work was supported by Swiss National Science Foundation grant 320030-120579. The Cartool software (http://brainmapping.unige.ch/Cartool.htm) has been programmed by Denis Brunet, from the Functional Brain Mapping Laboratory, Geneva, Switzerland, and is supported by the Center for Biomedical Imaging (CIBM) of Geneva and Lausanne.
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