Optimization of Electrode Configuration for the Removal of Eye Artifacts with Adaptive Noise Cancellation.

Scalp electroencephalography (EEG) is a neural source signal that is extensively used in neuroengineering due to its non-invasive nature and ease of collection. However, a drawback to the use of EEG is the prevalence of physiological artifacts generated by eye movements and eye blinks that contaminate the brain signals. Previously, we have proposed and validated an H ^∞ -based Adaptive Noise Cancellation (ANC) technique for the real-time identification, learning and removal of eye blinks, eye motions, amplitude drifts and recording biases from EEG simultaneously. However, the standard electroocu-lography (EOG) electrode configuration requires four electrodes for EOG measurement, which limits its applicability for reduced-channel mobile applications, such as brain-computer interfaces (BCI). Here, we assess multiple configurations with varying number of EOG electrodes and compare the ANC effectiveness of these configurations to the ideal four-electrode configuration. From an analysis of the root mean squared error (RMSE) and differences in signal to noise ratios (SNR) between the ideal four-electrode case and the alternative configurations, it is reported that several three-electrode alternative configurations were effective in essentially replicating the ability to remove EOG artifacts in an experimental cohort of ten healthy subjects. For nine subjects, it was shown that only two to three EOG electrodes were needed to achieve similar performance as compared to the four-electrode case. This study demonstrates that the typical four-electrode configuration for EOG recordings for adaptive noise cancellation of ocular artifacts may not be necessary; by using the proposed new EOG configurations it is possible to improve electrode allocation efficiency for EOG measurements in mobile EEG applications.