Frontiers in Human Neuroscience (Oct 2012)
Combining EEG and eye tracking: Identification, characterization and correction of eye movement artifacts in electroencephalographic data
Abstract
Eye movements introduce large artifacts to electroencephalographic recordings (EEG) and thus render data analysis difficult or even impossible. Trials contaminated by eye movement and blink artifacts have to be discarded, hence in standard EEG-paradigms subjects are required to fixate on the screen. To overcome this restriction, several correction methods including regression and blind source separation have been proposed. Yet, there is no automated standard procedure established. By simultaneously recording eye movements and 64-channel-EEG during a guided eye movement paradigm, we show that eye movement artifacts consist of several components, which arise from different sources. These include corneo-retinal dipole changes, saccadic spike potentials and eyelid movements. Moreover, we demonstrate that depending on electrode site, gaze direction and choice of reference these components contribute differently to the measured signal. Therefore they cannot be removed by regression-based correction methods, as these inevitably over- or under-correct individual artifact components. Finally we propose a correction procedure based on Independent Component Analysis (ICA). This procedure uses eye tracker information to reliably and objectively identify eye-artifact related ICA-components in an automated manner. We demonstrate that this approach allows removing or substantially reducing ocular artifacts including microsaccades without affecting the signal originating from brain sources. In conclusion the proposed method does not only provide a tool for detecting and correcting eye artifacts in standard EEG-paradigms but it also permits to study EEG-activity during eye tracking experiments and thus to investigate neural mechanisms of eye movement control and visual attention under natural conditions.
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