Windowed local area average reference filter for increasing the spatial resolution of EEG signals

Abstract

The phenomenon of transmitting the electric fields from their primary bioelectric sources through biological tissues towards measurement sensors is known as the volume conduction. It is a basis for the operation of many biosensors used for the measurement of bioelectromagnetical phenomena. An electroencephalograph (EEG) used for the recording and monitoring the bioelectrical activity of brain is an excellent example of such devices. Due to the volume conduction an overlapping of the contribution to each electrode channel from neighbouring bioelectrical sources is present. As a result raw EEG scalp potentials are characterized by weak spatial resolution. Owning to described reasons raw, multichannel EEG recordings tend to provide an unclear image of the brain activity. In this study the influence of applying 2D window function to the neighbourhood of each electrode before removal of common reference locally is examined. The Local Spatial Filters focus on removing the common signal of electrodes in a specific neighbourhood of the electrode of interest. The performance of algorithm is examined in the task of eliminating source overlapping from EEG-based BCI system recordings and compared with classic approaches such as Common Average Reference (CAR), Surface Laplacian (SL) and Common Spatial Pattern (CSP). For the better evaluation of performance of proposed approaches performance, their effectiveness is tested during the classification of the dataset IVa provided for the “BCI Competition III” organized by the Berlin Brain-Computer Interface group.