  Recent advances in wireless body area sensor net- works leverage wireless and
mobile communication technologies to facilitate development of innovative
medical applications that can significantly enhance healthcare services and
improve quality of life. Specifically, Electroencephalography (EEG)-based
applications lie at the heart of these promising technologies. However, the
design and operation of such applications is challenging. Power consumption
requirements of the sensor nodes may turn some of these applications
impractical. Hence, implementing efficient encoding schemes are essential to
reduce power consumption in such applications. In this paper, we propose an
analytical distortion model for the EEG-based encoding systems. Using this
model, the encoder can effectively reconfigure its complexity by adjusting its
control parameters to satisfy application constraints while maintaining
reconstruction accuracy at the receiver side. The simulation results illustrate
that the main parameters that affect the distortion are compression ratio and
filter length of the considered DWT-based encoder. Furthermore, it is found
that the wireless channel variations have a significant influence on the
estimated distortion at the receiver side.
