BCI-KurtusKahleFL2011

Abstract

The purpose of this project is to determine the optimal spatial filtering techniques for a Brain-Computer Interface (BCI) using an Emotiv Epoc EEG headset. Each type of spatial filter is a tool used to improve the characteristics of a signal using different patterns of linear combinations of electrodes. Those combinations produce a new signal with less noise, which gives BCI patients better control over the device.

In general, the methods used are a simple ear-reference, Bipolar filters, Common Average Reference (CAR) filters, and Laplacian filters. Bipolar filters combine any two electrodes. CAR filters use the mean of every electrode's voltage at a given time as their zero. Laplacian filters use several near neighbor electrodes to approximate a spatial second derivative of the signal. While the Emotiv headset offers several advantages for BCI applications, its limited number of electrodes makes generating accurate signals much more difficult, particularly for Laplacian filters. Obtaining high-quality signals given these limitations is the primary goal of this investigation.

Methods

There are two methods of spatial filtering being investigated. First, those currently applied with clinical EEG caps, namely Bipolar, Laplacian, and CAR. An additional "Half CAR" filter will be investigated. For this filter, the reference for a given electrode will be the average of the electrodes in its respective hemisphere. This filter may be more effective than full CAR if there is noise specific to one hemisphere, which may result from the brain signaling muscle motions to one side of the body. The laplacian filters need to be modified for this headset. Normally, one electrode would be taken from each of 4 sides of the electrode under investigation, to show how the signal changes with respect to the sampling location. The Emotiv Epoc headset does not have electrodes in the necessary locations for this procedure, so for this investigation, as many nearest electrodes as possible (either two or three) will be used. For each of these filters, R^2 values will be computed and compared.

The second method is directly optimizing for linear combinations of electrodes. Using MATLAB, it is possible to find local maxima (and ideally the global maximum) of R^2 based on the variations in linear combination.

Still to do

• Determine the effectiveness of each filter created for the Emotiv headset.

• Code a script to optimize possible linear combinations of electrodes for R^2.