The role of brain signal processing and neuronal modelling in epilepsy – a review

Epilepsy is a neurological disorder characterized by recurrent seizures due to spontaneous changes of chemical synaptic coupling within the central nervous system. Numerous studies have been done in order to increase the level of cognition in epilepsy. Electroencephalography (EEG) as a non-invasive technique with the ability of presenting potentials on the head surface due to neural activity is widely used in epilepsy studies. The signals have been analyzed by brain signal processing techniques which mainly are categorized in feature extraction, feature dimensionally reduction and classification. The limitations such as inapproachability to intracranial in vivo and few seizure occurrences during sampling led to investigate on a model of signals and neural activity. This paper reviews the fundamentals of epilepsy toward using brain signal processing and neuronal modeling in three major branches; detection, prediction and source localization. It resulted a rare number of investigations on seizure epilepsy prediction due to the lack of long-term epilepsy EEG recording ending to the seizure. Subsequently, this review paper suggests to consider brain signal processing techniques in sub-branches of epilepsy detection; status, type, markers and surface localization, whilst it plays a remarkable role targeting to the source localization by neuronal modeling