A 16 Channel Real-Time EEG Processing Based on ORICA Algorithm using 28nm CMOS Technology
Kai-Yen Wang, Yun-Lung Ho, Yu-De Huang, Wai-Chi Fang · 2018
In this paper, we propose a system-on-chip(SOC) design of highly effective multi-channel real-time EEG signal processing system based on Online-Recursive Independent Component Analysis (ORICA) algorithm implemented using TSMC's 28nm CMOS technology. In this chip, concepts of system-on-chip (SOC) design and effective system integration technique are well-combined together to realize a highly miniaturized real-time EEG processing system.. The core area and total power consumption of the chip are respectively 1246*1246μm2 and 25.03mW. The chip operations were validated by ADVANTEST V93000 PS1600 and the results obtained match with the software simulation. The average correlation coefficient between original source signals and extracted ORICA signals reaches 0.9572. Eye blink artifact, and facial muscle artifact will be removed automatically. Producing a pure EEG signal is beneficial for real-time data analysis; therefore, this chip design can enhance the reliability and feasibility of EEG-related applications, such as BCI, medical diagnosis and depth of anesthesia detection.