Real-time ocular artifacts removal of EEG data using a hybrid ICA-ANC approach

Removal of ocular artifacts (OA) in real-time is an essential component in electroencephalography (EEG) based brain computer interface (BCI) applications. However, many proposed artifact removal methods are not applicable in real-time applications due to their time-consuming process. In this paper we propose a hybrid approach based on a new combination of independent component analysis (ICA) and adaptive noise cancellation (ANC). A particularly new feature of the proposed approach is the utilization of the ICA decomposition to extract the artifact source signal to be used in ANC based on neural networks. The method performs using a few EEG signals without requiring any additional electrodes (e.g. electrooculography). We show that the proposed approach is capable of effectively reducing the ocular artifacts in a negligible time delay well applicable in real-time BCI. In order to achieve reliable results, the proposed approach is evaluated using data recorded during cue-based BCI. The efficacy of the proposed approach in both offline and online performances is compared to several state of the art methods. The results demonstrate that the proposed approach outperforms the compared methods in terms of removal of OA and recovery of the underlying EEG.