A Proposed Algorithm and Sampling Conditions for Nonlinear Analysis of EEG

Shin, C J; Lee, K H; Choi, S K; Yoon, I Y

Abstract

OBJECTIVES
With the object of finding the appropriate conditions and algorithms for dimensional analysis of human EEG. We calculated correlation dimensions in the various conditions of sampling rate and data aquisition time and improved the computation algorithm by taking advantage of bit operation instead of log operation.
METHODS
EEG signals from 13 scalp lead of a man were digitized with A-D converter under the condition of 12 bit resolution and 1000Hertz of sampling rate during 32 seconds. From the original data, we made 15 time series data which have different sampling rate of 62.5, 125, 250, 500, 1000 hertz and data acqusition time of 10, 20, 30 second, respectively. New algorithm to shorten the calculation time using bit operation and the Least Trimmed Squares(LTS) estimator to get the optimal slope was applied to these data.
RESULTS
The values of the correlation dimension showed the increasing pattern as the data acquisition time becomes longer. The data with sampling rate of 62.5 Hz showed the highest value of correlation dimension regardless of sampling time but the correlation dimension at other sampling rates revealed similar values. The computation with bit operation instead of log operation had a statistically significant effect of shortening of calculation time and LTS method estimated more stably the slope of correlation dimension than the Least Squares estimator.
CONCLUSION
The bit operation and LTS methods were successfully utilized to time-saving and efficient calculation of correlation dimension. In addition, time series of 20-sec length with sampling rate of 125Hz was adequate to estimate the dimensional complexity of human EEG.