The electroencephalogram (EEG) is a widely used traditional procedure for diagnosing, monitoring and managing neurological disorders. Many artifact types that often contaminate EEG remain a key challenge for precise diagnosis of brain dysfunctions and neurological disorders. Hence, artifact removal is intuitively required for accurate EEG analysis and treatment. This paper presents a new extensive method that can remove a wide variety of EEG artifacts based mainly on Template Matching approach including multiple signal-processing tools. The method was evaluated and validated on real EEG data, giving promising results that offer better capabilities to neurophysiologists in routine EEG examinations and diagnosis.

Epilepsy is one of the most prevalent neurological disorders with no age, racial, social class, and neither national nor geographic boundaries. There are 50 million sufferers in the world today with 2.4 million new cases occur each year. Electroencephalogram (EEG) has become a traditional procedure to investigate abnormal functioning of brain activity. Epileptic EEG is usually characterized by short transients and sharp waves as spikes. Identification of such event splays a crucial role in epilepsy diagnosis and treatment. The present study proposes a method to detect three epileptic spike types in EEG recordings based mainly on Template Matching Algorithm including multiple signal-processing approaches. The method was applied to real clinical EEG data of epileptic patients and evaluated according to sensitivity, specificity, selectivity and average detection rate. The promising results illuminate that hybrid processing approaches in temporal, frequency and spatial domains can be a real solution to identify fast EEG transients.