Many neuroscientists conjecture that dynamic brain behaviors result from the interactions of neurons and assemblies of neurons that form at multiple spatial scales (Nunez, 1995, 2000a,b). In particular, the large-scale integration of different brain areas is believed to be required for the manifestation of a complete cognitive structure (Varelaet al., 2001). A fundamental problem in modern neuroscience is to understand how this integration is achieved.
Several lines of evidence point to a fundamental role of neural synchrony in the brain functional integration and segregation (Varelaet al., 2001; Nunez, 2000a; Klimesch, 1996).
Neural synchrony refers to the phenomenon in which neurons coding for a common representation synchronize their oscillatory firing activity within a restricted frequency band (Trujilloet al., 2005; Varelaet al., 2001).
There are two main approaches to the study of neural synchrony with EEG which provide complementary information about the underlying neuronal processing (Le Van Quyen and Bragin, 2007): the spectral power and the phase synchronization analyses. The analysis of the spectral power of the oscillatory activity focuses on the local synchrony of neuronal populations (synchrony within neighboring cortical areas [Section 3.2.4]), whereas the phase synchronizationanalysis refers to the phase coupling between neuronal assemblies, which lie either within the same hemisphere or at different hemispheres (Section 3.2.5).
At the scale of local oscillatory activity, the theta oscillations and the 30–70 Hz gamma rhythm have been shown to have particularly strong behavioral correlates (Kahana, 2006). For instance, strong theta spectral power is found in working memory tasks in humans and in the hippocampus of animals during locomotion, orienting, and other voluntary behaviors (Kahana, 2006).
High frequency gamma oscillations have been most prominently associated with top-down attentional processing and object perception (Rodriguezet al., 1999; Tallon-Baudryet al., 1997).
At the larger scale of long-range interactions between distant brain regions for complex multiple integration, a particular emphasis has been placed on the relevance of 8–13 Hz alpha and 4–7 Hz theta synchronization (von Stein et al., 2000; von Stein and Sarnthein, 2000; Herrojo Ruizet al., 2009).
The functional interaction between distant brain regions is believed to
be best characterized by transient phase relationships between the oscilla-tory activities of underlying neuronal populations, termed as phase syn-chronization (Tasset al., 1998; Varelaet al., 2001; Sausenget al., 2005). In a number of studies however, large-scale functional coupling in EEG signals has been investigated using classical magnitude-squaredcoherencefunction (Schacket al., 1999; Srinivasanet al., 1998a; von Steinet al., 2000; von Stein and Sarnthein, 2000) – which is equivalent to a correlation coefficient in the frequency domain – as a linear measure of bivariate synchronization.
Magnitude-squared-coherence mixes both phase and amplitude correlation (Womelsdorf et al., 2007), and there exists no clear-cut way to extract the phase synchronization information from the coherence function (Lachaux et al., 1999). Further, Tass et al.(1998) pointed out that synchronization of two oscillators is not equivalent to the linear correlation of two signals that is measured by coherence. For instance, if the oscillators are chaotic and their amplitudes are varying without any temporal correlation yet their phases are coupled, coherence would be very low and it would not be possible to detect synchronization of their phases. Since phase synchronization requires an adjustment of phases but not of amplitudes, it is a more general approach to address the study of synchronization between EEG signals. This is the approach that I will use in my thesis for the study of large-scale interactions between EEG signals.
Methods
3.1 EEG and EMG data processing
In the first EEG experiment, artifact rejection was visually performed off-line trial-by-trial to exclude segments contaminated with eye movements or other instrumental or muscular artifacts. In the second and third EEG experiments, however, I used a new advanced tool of automatic artifact correction, wavelet-enhanced independent component analysis (Castellanos et al., 2006; Wallstromet al., 2004), which I describe in the following.
Independent component analysis (ICA) and wavelet-enhanced ICA Standard ICA is commoly used to obtain the statistically independent com-ponents of raw EEG signals. The user then rejects the ICA comcom-ponents which contain artifacts, and the rest of the ICA components are transformed back into the signal space in order to obtain EEG signals without artifacts.
However, the rejection of the ICA components has been proven to constitute a loss of neural activity, because the rejected components do not always contain only artifacts: they contain also cerebral activity. This might then affect the data analysis and lead to spurious results (Castellanoset al., 2006;
Wallstromet al., 2004). Consequently, wavelet-enhanced ICA is an algorithm designed to improve the “leak” of the cerebral activity by separating the background neural activity from the isolated artifacts in the ICA components.
This is possible by means of wavelet thresholding as anintermediatestep to the demixed independent components. The wavelet thresholding filters out
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the artifacts only due to their specific time-frequency properties and leaves the background neural activity “untouched”. Because this procedure can be performed automatically by filtering all independent components ren-dered by ICA, wavelet-enhanced ICA does not require the laborious visual inspection of all ICA components and is, accordingly, a faster procedure.
After applying ICA, a final visual inspection of the data can be performed to eliminate epochs still containing muscle artifacts.