@inproceedings{7a45e099f52f424f949139fbf4c17062,
title = "A SOC Design of ORICA-based Highly Effective Real-time Multi-channel EEG System",
abstract = "Independent component analysis (ICA) has been wildly used to improve EEG based application such as brain computer interface (BCI). However, some well know ICA algorithm, such as Infomax ICA, suffering from the problem of convergence latency and make it hard to be apply on real-time application. This paper proposes a highly efficient chip implementation of multi-channel EEG real-time system based on online recursive independent component analysis algorithm (ORICA). The core size of the chip is 1.5525-mm2 using 28nm CMOS technology. The EEG demonstration board will be implemented with the ORICA chip. The operation frequency and power consumption of the chip are 100 MHz and 17.9 mW respectively. The proposed chip was validated with a real-time circuit integrated system and the average correlation coefficient between simulations results and chip processing results is 0.958.",
keywords = "Biomedical signal processing, Brain-computer interface (BCI), Online recursive independent component analysis (ORICA), Real-time system",
author = "Ho, {Yun Lung} and Huang, {Yu De} and Wang, {Kai Yen} and Fang, {Wai Chi}",
year = "2019",
month = jul,
doi = "10.1109/EMBC.2019.8856299",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4762--4765",
booktitle = "2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019",
address = "United States",
note = "41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 ; Conference date: 23-07-2019 Through 27-07-2019",
}