Compressing EEG Data Using Adaptive Function Approximation

Abstract

This paper proposes a compression method for storing the electroencephalogram (EEG) waveforms into digital storages under the condition of keeping the quality sufficient for diagnosis.
The method is designed on the basis of features of the EEG waveforms. The data volume is reduced by storing the coefficients of the functions approximating the EEG waveforms. The waveforms are divided into segments, in each of which the frequency is similar, and are decomposed into basal wave components and other superimposed ones for efficient compression.
Effectiveness of the method has been evaluated by applying it to several EEG waveforms. Quality of the reconstructed waveforms is band width of 0.5_??_120Hz, dynamic range of 60dB, and maximum error of 2μV_p-p. The compression ratio is 0.648 on the average.