EEG analysis using chi-square association metric

Abstract

The detection of neural spike activity is a technical challenge that needs to be studied for examining many types of brain functions. This paper presents some aspects of fast non-linear association measures, based on the work of Cramer (V) and Sakoda’s adjusted Pearson’s C, (C*). The features of these new measures like the V measure and Sakoda’s measure are manifested when applied to bio-signals like Electroencephalographic (EEG) signals. A comparative study with other well known association measures of EEG signals is presented. Based on the analysis of the experimental results it can be concluded that Sakoda’s adjusted Pearson’s measure produces better results than other measures. The following results are also verified (1) Chi-squared --(χ2) value which is approximately equal to 2N times the Mutual Information (Ml) and (2). The value of the Information Transmission coefficient Txy without and with V yielded approximately the same result. This proposal aims at optimizing the resources such as time and memory storage by reducing the number of electrodes. © 2008 by the IETE.