This paper introduces the use of wavelet analysis to follow the temporal variations in the coupling between oscillatory neural signals. Coherence, based on Fourier analysis, has been commonly used as a first approximation to track such coupling under the assumption that neural signals are stationary. Yet, stationary neural processing may be the exception rather than the rule. In this context, the recent application to physical systems of a wavelet-based coherence, which does not depend on the stationarity of the signals, is highly relevant. This paper fully develops the method of wavelet coherence and its statistical properties so that it can be practically applied to continuous neural signals. In realistic simulations, we show that, in contrast to Fourier coherence, wavelet coherence can detect short, significant episodes of coherence between non-stationary neural signals. This method can be directly applied for an ‘online’ quantification of the instantaneous coherence between two signals.

Key words: wavelet, coherence, synchrony, EEG, LFP

This paper introduces a new method, single-trial phase locking statistics (S-PLS) to estimate phase locking in single trials of brain signals between two electrodes. The possibility of studying single trials removes an important limitation in the study of long-range synchrony in brain signals. S-PLS is closely related to our previous method, phase locking statistics (PLS) that estimates phase locking over a set of trials. The S-PLS method is described in detail and applied to human surface recordings during the task of face-recognition. We compare these results with those provided by PLS and show that they are qualitatively very similar, although S-PLS provides better discrimination of synchronic episodes.

Excerpt: Based on […] new neurophenomenological circulation, we review here some of the ongoing work of our research group concerning epilepsy. In particular, special attention is here paid to interdependence of neurodynamic and phenodynamic structures associated with the beginning of an epileptic seizure, the so-called aura.

Neurophenomenology (Varela 1996) is not only philosophical but also empirical and experimental. Our purpose in this article is to illustrate concretely the efficiency of this approach in the field of neuroscience and, more precisely here, in epileptology. A number of recent observations have indicated that epileptic seizures do not arise suddenly simply as the effect of random fluctuations of brain activity, but require a process of pre-seizure changes that start long before. This has been reported at two different levels of description: on the one hand, the epileptic patient often experiences some warning symptoms that precede seizures from several minutes to hours in the form of very specific lived events. On the other hand, the analyses of brain electrical activities have provided strong evidence that it is possible to detect a pre-seizure state in the neuronal dynamics several minutes before the electro-clinical onset of a seizure. We review here some of the ongoing work of our research group concerning seizure anticipation. In particular, we discuss experimental evidence of upward (local-to-global) formation of conscious experience and its neural substrate, but also of the downward (global-to-local) determination of local neuronal activity by situated conscious activity and its substrate large-scale neural assemblies. This causal role of conscious experience may lead to new kinds of therapy for epileptic patients.

Recent studies have shown that non-linear analysis of intracranial activities can detect a ‘pre-ictal phase’ preceding the epileptic seizure. Nevertheless, the dynamical nature of the underlying neuronal process and the spatial extension of this pre-ictal phase still remain unknown. In this paper, we address these aspects using a new non-linear measure of dynamic similarity between different parts of intracranial recordings of nine patients with medial temporal lobe epilepsy recorded during transitions to seizure. Our results confirm that non-linear changes in neuronal dynamics allow, in most cases (16 out of 17), a seizure anticipation several minutes in advance. Furthermore, we show that the spatial distribution of pre-ictal changes often involves an extended network projecting beyond the limits of the epileptogenic region. Finally, the pre-ictal phase could frequently (13 out of 17) be characterized with a marked shift toward slower frequencies in upper delta or theta frequency range.

Key words: long-term non-stationarity, non-linear analysis, pre-ictal phase, seizure anticipation, temporal lobe epilepsy

The quantification of phase synchrony between neuronal signals is of crucial importance for the study of large-scale interactions in the brain. Two methods have been used to date in neuroscience, based on two distinct approaches which permit a direct estimation of the instantaneous phase of a signal [Phys. Rev. Lett. 81 (1998) 3291; Human Brain Mapping 8 (1999) 194]. The phase is either estimated by using the analytic concept of Hilbert transform or, alternatively, by convolution with a complex wavelet. In both methods the stability of the instantaneous phase over a window of time requires quantification by means of various statistical dependence parameters (standard deviation, Shannon entropy or mutual information). The purpose of this paper is to conduct a direct comparison between these two methods on three signal sets: (1) neural models; (2) intracranial signals from epileptic patients; and (3) scalp EEG recordings. Levels of synchrony that can be considered as reliable are estimated by using the technique of surrogate data. Our results demonstrate that the differences between the methods are minor, and we conclude that they are fundamentally equivalent for the study of neuroelectrical signals. This offers a common language and framework that can be used for future research in the area of synchronization.

Key words: phase synchrony, neural synchrony, Hilbert transform, wavelet transform, surrogate data