Distributed eye blink artifact removal in a wireless EEG sensor network

In this paper, we present a distributed algorithm to remove eye blink artifacts from electroencephalography (EEG) signals recorded in a modular high-density EEG system, referred to as a wireless EEG sensor network (WESN). A WESN is a particular instance of a wireless body area network for long-term non-invasive neuromonitoring, which is amenable to extreme miniaturization and low-power system design. We first propose a centralized algorithm for eye blink artifact removal (EBAR) based on the multi-channel Wiener filter (MWF). We then show how this MWF-based EBAR algorithm can be implemented in a distributed fashion to remove the eye blink artifacts in each EEG node without centralizing all the raw EEG data. Instead, the EEG nodes share fused EEG signals with each other. Nevertheless, it can be shown that the estimation performance of the distributed algorithm is equivalent to the performance of the centralized MWF, as if each EEG node had access to all the EEG channels of the WESN. This is also experimentally validated by means of recorded EEG data.