This paper describes the use of a computational tool based on the Morlet wavelet transform to investigate the interaction dynamics between oscillations generated by two anatomically distinct neuronal populations. The tool uses cross wavelet transform, coherence, bi-spectrum/bicoherence and phase synchronization. Using specimen data recorded from the hippocampus of a rat with experimentally induced focal epilepsy, linear and non-linear correlations between neuronal oscillations in the CA1 and CA3 regions have been computed. The results of this real case study show that the computational tool can successfully analyse and quantify the temporal interactions between neuronal oscillators and could be employed to investigate the mechanisms underlying epilepsy.

Frequency analysis of neuronal oscillation is very important for understanding the neural information processing and mechanism of disorder in the brain. This Letter addresses a new method to analyze the neuronal population oscillations with empirical mode decomposition (EMD). Following EMD of neuronal oscillation, a series of intrinsic mode functions (IMFs) are obtained, then Hilbert transform of IMFs can be used to extract the instantaneous time frequency structure of neuronal oscillation. The method is applied to analyze the neuronal oscillation in the hippocampus of epileptic rats in vivo, the results show the neuronal oscillations have different descriptions during the pre-ictal, seizure onset and ictal periods of the epileptic EEG at the different frequency band. This new method is very helpful to provide a view for the temporal structure of neural oscillation.

The prediction of epileptic seizures can promise a new diagnostic application and a novel approach for seizure control. This paper proposes an improved dynamical similarity measure to predict epileptic seizures in electroencephalographic (EEG). First, mutual information and Cao’s method are employed to reconstruct a phase space of preprocessed EEG recordings by using the positive zero crossing method. Second, a Gaussian function replaces the Heavyside function within correlation integral at calculating a similarity index. The crisp boundary of the Heavyside function is eliminated because of the Gaussian function’s smooth boundary. Third, an adaptive detection method based on the similarity index is proposed to predict the epileptic seizures. In light of test results of EEG recordings of rats, it is found that the new dynamical similarity index is insensitive to the selection of the radius value of Gaussian function and the size of segmented EEG recordings. Comparing with the dynamical similarity index proposed by Le Van Quyen et al. [Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings, NeuroReport 10 (1999) 2149–2155], the tests of twelve rats show the new dynamical similarity index is better to predict the epileptic seizures.