1. Introduction
EEG records carry information about abnormalities or responses to certain stimuli in the human brain. Some of the characteristics of these signals are the frequency and the morphology of their waves.
These components are in the order of just a few up to 200 μV, and their frequency content differs among the different neurological rhythms, as the alpha, beta, delta and theta rhythms [1].
Such rhythms are analyzed by physicians in order to detect neural disorders and cerebral pathologies [2]. However, these rhythms are generally mixed with other biological signals, for example alpha is commonly mixed with the EOG (electro-oculogram). In this case, opening, closing or movements of the eyes produce artifacts in the EEG. Other artifact sources are the ECG (electrocardiogram), EMG (electromyogram) and the power line interference (50 or 60 Hz) [3]. An example of an EEG mixed with ECG and corrupted with line interference is illustrated in Figure 1.
Due to the presence of artifacts, it is difficult to analyze the EEG, for they introduce spikes which can be confused with neurological rhythms. Thus, noise and undesirable signals must be eliminated or attenuated from the EEG to ensure a correct analysis and diagnosis.
In this work, we propose a cascade of adaptive filters in order to remove some frequent artifacts in EEG signals. The aim of these filters is to cancel ECG, EOG and line interference.