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Related Experiment Video

Updated: Dec 30, 2025

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

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A SOC Design of ORICA-based Highly Effective Real-time Multi-channel EEG System.

Yun-Lung Ho, Yu-De Huang, Kai-Yen Wang

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 18, 2020

Summary

This study introduces a novel, efficient chip for real-time electroencephalography (EEG) analysis using the online recursive independent component analysis (ORICA) algorithm. The ORICA chip enables faster processing for brain-computer interfaces (BCIs) and other EEG applications.

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Area of Science:

Biomedical Engineering
Signal Processing
Computer Engineering

Background:

Independent Component Analysis (ICA) is crucial for enhancing electroencephalography (EEG) applications like brain-computer interfaces (BCIs).
Traditional ICA algorithms, such as Infomax ICA, face convergence latency issues, hindering real-time applications.

Purpose of the Study:

To propose and implement a highly efficient chip for a multi-channel EEG real-time system.
To address the limitations of existing ICA algorithms in real-time EEG processing.

Main Methods:

Development of a novel online recursive independent component analysis (ORICA) algorithm.
Design and fabrication of a compact 1.5525-mm² chip using 28nm CMOS technology.
Integration of the ORICA chip into an EEG demonstration board for real-time system validation.

Main Results:

The chip operates at 100 MHz with a power consumption of 17.9 mW.
Validation with a real-time integrated system demonstrated high performance.
An average correlation coefficient of 0.958 was achieved between simulation and chip processing results.

Conclusions:

The proposed ORICA chip offers a highly efficient solution for real-time multi-channel EEG processing.
This advancement facilitates the practical application of ICA in real-time EEG systems, including BCIs.
The chip's performance metrics indicate its suitability for demanding, time-sensitive biomedical applications.