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In symbolic dynamics, the definition of a symbolic sequence from a continuous times series depends on the use of an appropriate partition of the phase space. In fact, the best way is to estimate a generating partition. However, it is not possible to find generating partitions for most experimental observations because such partitions do not exist when noise is present. In this paper, different partition methods applied to stochastic and chaotic system will be compared in order to choose one which conserves system entropy rate. This partition is called a Markov partition. General Terms:Markov partition, symbolic dynamic

In this paper, we use Consensus version of the matching pursuit algorithm (CMP) which fit to the noisy Evoked Potential signal persistent in all responses (trial). The Evoked Potential EP is a specific wave resulted from a stimulus. This research is performed in a highly redundant timefrequency dictionary of Gabor functions.For phase-locked Evoked Potential trials, we conclude that the morphology of EP signal obtained by a reconstructed signal can be very well explained with a good quality of energy ratio factor (QR). However for a noisy and jitter EP, we couldn't optimally reconstruct our original data due to random atoms of CMP dictionary. So our strategy consists to select only the significant atoms to rebuild an EP signal. Using this test, we can reach a good QR ratio.

To define the neural networks responsible of an epileptic seizure, it is useful to perform advanced signal processing techniques. In this context, electrophysiological signals present three types of waves: oscillations, spikes, and a mixture of both. Recent studies show that spikes and oscillations should be separated properly in order to define the accurate neural connectivity during the pre-ictal, seizure and inter-ictal states. Retrieving oscillatory activity is a sensitive task due to the frequency overlap between oscillations and transient activities. Advanced filtering techniques have been proposed to ensure a good separation between oscillations and spikes. It would be interesting to apply them in real time for instantaneous monitoring, seizure warning or neurofeedback systems. This requires improving execution time. This constraint can be overcome using embedded systems that combine hardware and software in an optimized architecture.We propose here to implement a stationary wavelet transform (SWT) as an adaptive filtering technique retaining only pre-ictal gamma oscillations, as validated in previous work, on a partial dynamic configuration. Then, the same architecture is used with further modifications to integrate spatio temporal mapping for an early recognition of seizure build-up.Data that contains transient, pre-ictal gamma oscillations and a seizure was simulated. the method on real intracerebral signals was also tested. The SWT was integrated on an embedded architecture. This architecture permits a spatio temporal mapping to detect the accurate time and localization of seizure build-up, while reducing computation time by a factor of around 40. Embedded systems are a promising venue for real-time applications in clinical systems for epilepsy.

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