Assessing electroencephalographicrecordings for epilepsy patients at differentscales of spatial resolution

Abstract

The characterization of neuronal activity is challenging due to the intrinsic complexity of The brain and its dynamics. It is important to understand the activity in brain functions and also in brain diseases. Epilepsy is of major interest for scientists since decades. The characterization of the brain area that initiates the epileptic seizures and how they propagate through the brain is still subject to ongoing research. Evolution of technology enables us to have more precise and effective recording devices to acquire the electrical signals from the brain. For the case of epilepsy diagnostics this includes the use of intracranial electrodes implanted in the brain. Apart from conventional intracranial macro electroencephalographic (EEG) recordings, we now also have intracranial micro EEG recordings. Such microelectrodes can record electrical activity at a subImillimetre scale in the brain and therefore are a valuable source of information to do further analysis. This thesis project was carried out in the Nonlinear Time Series Analysis (NTSA) research group in the Pompeu Fabra University (UPF) and involved an internship in the epileptology department in theclinic of the University of Bonn. This internship allowed us to acquire micro and macro EEG recordings and also allowed to analyse in situ the problems that arise during the data acquisition process. One main contribution of this project is the acquisition of this innovative type of data for further analysis. The data analysed in this work is from an epilepsy patient with medial temporal lobe (MTL) epilepsy. In this thesis we assess the EEG on different scales of spatial resolution, comparing macro and micro recordings. Furthermore, we analyse the synchronicity during the course of an epileptic seizure between neural populations. Macro recordings show a drop of synchrony at the seizure onset, consistent with previous findings of the Nonlinear Time Series Analysis group. In contrast, micro recordings show an increase of Synchrony at the onset of the seizure. These findings, which are new and original to this thesis, suggest that only very local networks are highly synchronized while the seizure is taking place. These findings can contribute to the understanding of epileptic seizures’ dynamics.