Energy efficiency and reliability in wireless biomedical implant systems.

The use of wireless implant technology requires correct delivery of the vital physiological signs of the patient along with the energy management in power-constrained devices. Toward these goals, we present an augmentation protocol for the physical layer of the medical implant communications service (MICS) with focus on the energy efficiency of deployed devices over the MICS frequency band. The present protocol uses the rateless code with the frequency-shift keying (FSK) modulation scheme to overcome the reliability and power cost concerns in tiny implantable sensors due to the considerable attenuation of propagated signals across the human body. In addition, the protocol allows a fast start-up time for the transceiver circuitry. The main advantage of using rateless codes is to provide an inherent adaptive duty cycling for power management, due to the flexibility of the rateless code rate. Analytical results demonstrate that an 80% energy saving is achievable with the proposed protocol when compared to the IEEE 802.15.4 physical layer standard with the same structure used for wireless sensor networks. Numerical results show that the optimized rateless coded FSK is more energy efficient than that of the uncoded FSK scheme for deep tissue (e.g., digestive endoscopy) applications, where the optimization is performed over modulation and coding parameters.

Statistical characterization of complex-valued EEG spectrum during mental imagery tasks.

Electroencephalogram (EEG) recordings of brain activities can be processed in order to augment the brain's cognitive, sensory, or motor functionality. A representative, yet analytically tractable, model is essential to EEG processing. Several studies have examined different statistical models for EEG power spectrum. But recent studies have shown that not only the power, but also the phase of the spectrum, carries relevant information on brain activities. As a result, this paper focuses on the complex-valued spectrum of EEG, and proposes a general non-circularly-symmetric multivariate Gaussian model for this spectrum. This simple model can encapsulate the information in both power and phase of the spectrum, and its validity for EEG data has been verified using standard statistical tests.