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1.
Annu Int Conf IEEE Eng Med Biol Soc ; 2019: 664-667, 2019 Jul.
Article in English | MEDLINE | ID: mdl-31945985

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.


Subject(s)
Electroencephalography , Algorithms , Brain-Computer Interfaces , Computer Systems , Signal Processing, Computer-Assisted
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2019: 4762-4765, 2019 Jul.
Article in English | MEDLINE | ID: mdl-31946926

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.


Subject(s)
Brain-Computer Interfaces , Electroencephalography , Signal Processing, Computer-Assisted , Algorithms , Computer Systems , Humans
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