EEG ocular artifact removal through ARMAX model system identification using extended least squares

Abstract

Abstract. The removal of ocular artifact from scalp electroencephalograms (EEGs) is of considerable importance for both the automated and visual analysis of underlying brainwave activity. Traditionally, subtraction techniques use linear regression to estimate the influence of eye movements on the electrodes of interest. These methods are based on the assumption that the underlying brainwave activity is uncorrelated when, in general, it is not. Furthermore, regression methods assume that the ocular artifact propagation is frequency independent, i.e. all waveforms of the ocular artifact propagate similarly. In this paper, we examine relaxing these assumptions by using a more general autoregressive (AR) moving average (MA) exogenous (X) model and the extended least squares (ELS) algorithm to remove ocular artifact. We demonstrate that in some cases this general ARMAX model can decrease ocular artifact not removable by standard regression techniques. We also show that the incorporation of a forgetting factor to exponentially weight past data can improve ocular artifact removal even for the traditional subtraction method.

A Brainwave Signal Measurement and Data Processing Technique for Daily Life Applications

“…Although several methods are proposed (Hass et al, 2003;Krishnaveni et al, 2006), it is difficult to separate artifacts such as OA from EEGs completely. In a series of measurements, even ignoring a small number of data which exhibit excessive responses supposedly by the artifact contaminated, since the measurement is focusing on the frequency components continuously being produced in the psychophysiological state, the statistical representation by the rest of the data can exhibit the gross characteristics of the state.…”

Teleoperation of an Industrial Manipulator Through a TCP/IP Channel Using EEG Signals

“…The data acquisition module is represented, basically, by an A/D converter. In pre-processing phase filters and algorithms are used to remove artifacts [9]. The feature extraction module gets the information and identify the EEG signal characteristics that will be used by the next module (classification) to make a decision.…”

Brain computer interface

“…During the signal processing stage, ocular artifacts are removed [12], [13] from the data by treating the measured EEG as a linear superposition of the measured EOG signals and the real EEG (1) Here, is the number of sites at which the EOG measurement is done, two in our setup. Since the dynamic range of is small in comparison to , we can use least square minimization to compute the propagation constants .…”