Online removal of ocular artifacts from single channel EEG for ubiquitous healthcare applications

A large number of ubiquitous healthcare systems and applications have been introduced recently as a result of breakthroughs in technologies such as wireless sensors and BAN (Body Area Network). However, most of the previous work has focused on the technology platform and service architecture for u-healthcare, and sensor data acquisition and handling have not received much attention. Readings from sensors are typically unreliable and contain a lot of noise, especially when they are used in uncontrolled environments such as ubiquitous healthcare systems. Without proper handling of noise, quality of service cannot be guaranteed. The problem is exacerbated for EEG signals; because the signal-to-noise ratio is especially low, the number of channels is limited, and noise (mostly ocular artifacts) removal should be done online. In this work, we introduce a method called Online SSA in order to address this problem. Online SSA extends the conventional offline SSA by incorporating the rank-1 modification technique to incrementally update the singular spectrum of the noise model. We validated the proposed method using real EEG data generated from a single channel EEG device. The results of the experiment demonstrated the effectiveness of the method.