A Preliminary Study on Automatic Detection and Filtering of Artifacts from EEG Signals

One of the biggest problems in EEG recordings is the contamination of the signal caused by artifacts because these interferences hinder the analysis of real neural information. Thus, their elimination while preserving as much brain data as possible is a key procedure before the study of the EEG. To address the automatic removal of craniofacial artifacts, this paper proposes a two-stages procedure: the former one is the detection stage - where both a MLP neural network and a dynamic threshold method are applied to detect the contaminated areas of the EEG-, while the latter is the removal stage - combining CCA and EEMD algorithms to remove the artifact data only. Experimental results show that both detection methods are comparable, but with the dynamic threshold detection slightly outperforming the MLP. Also, the combined technique can completely remove those artifacts scattered in all the EEG channels. This study will be extended to ocular artifacts, where more complex models would be required.