| Body sensor network (BSN) is a promising technology to monitor neurophysiological data in naturalistic settings. Existing BSNs with wearable Electroencephalogram (EEG) to record neurological activities have limitations in terms of modularity, scalability, and flexibility in deployment. Also, EEG signals are often contaminated with ocular artifacts (OA) like eye-blinks that must be cleaned prior to the signal analysis. The current OA denoising techniques require human supervision that limits automation.;The key objectives of this research were: (1) to design an unsupervised and fullyautomatic algorithm for denoising eye-blinks in multi-channel EEG, (2) to investigate EEG modularization by eliminating the requirement of driven-right-leg (DRL) circuit for the batteryoperated EEG and investigation of consequential effects, and (3) to develop a scalable and reconfigurable architecture for BSN with modular EEG nodes that can be deployed in a Legolike fashion.;A robust algorithm to denoise eye-blink artifacts was used which uses modified multiscale sample entropy and kurtosis to automatically identify the independent eye-blink artifactual components and subsequently denoise these components using wavelet decomposition. To evaluate the DRL effects, a single-channel battery-powered EEG with a new analog front end (AFE) was designed that can record neural signals with and without DRL. Furthermore, sensor-level modular EEG nodes with in situ AFE are prototyped and integrated into the network via digital (I2C) interface. These prototypes were validated against two commercially available EEG. |
