A Simple and Fast Algorithm for Automatic Suppression of High-Amplitude Artifacts in EEG Data

In this paper we present a simple and fast technique for correcting high amplitude artifacts that contaminate EEG signals. Examples of such artifacts are ocular movement, eye blinks, head movement, etc. Since the measured EEG data can be modeled as a linear combination of brain sources and artifacts, the proposed technique is based on multiplying the observed data matrix by a blocking matrix that has the effect of blocking high amplitude artifacts, while linearly transforming the other sources without any distortion. The advantages of using this technique are: 1) it is relatively fast, so it can be applied in real time, 2) it is completely automatic, and 3) can be successfully applied to signals which fail with ICA-based algorithms.