On the Use of Rough Sets for Artefact Extraction from EEG Datasets

High-density electroencephalography produces large volumes of data. The analysis of EEG data is confounded by the existed of a number of different artefacts such as eye blink and, muscle movement which impede the analysis of the data. Typically, artefacts are removed by visual inspection - an arduous task for high-density recordings. In addition, different researchers use Consistency across different laboratories is often difficult, and in addition, the task has to be repeated for each study. An automated method for artefact identification and removal would be a very useful tool for data processing in this domain. In this study, rough sets is employed as a means of automating artefact identification and removal within the context of EEG analysis using the EEGLAB analysis system. The results from this preliminary study indicate that artefacts can be identified and removed with approximately 85% accuracy.