A real-time processing flow for ICA based EEG acquisition system with eye blink artifact elimination
Kuan-Ju Huang, Jui-Chieh Liao, Wei-Yeh Shih, Chih-Wei Feng, Jui-Chung Chang, Chia-Ching Chou, Wai-Chi Fang · 2013
This paper presents a real-time processing flow for ICA based EEG acquisition system with eye blink artifact elimination. Since EEG signals are one of the feeblest physiological electrical signals, it is easily contaminated by artifacts. Previously, ICA was used to extract artifacts from an EEG data segment in a time period. After processing of ICA, automatic artifact detection and elimination are performed. After that, artifact free EEG signals are reconstructed. Recently, many kinds of EEG applications such as BCIs are proposed to control machines through EEG directly. In order to make BCIs more feasible and reliable, the EEG signals used for BCIs need to be acquired from human without artifacts in real-time. In this work, a real-time ICA algorithm, ORICA, is adopted. Since eye blink artifact dose the most significant harm to EEG signals, this work focus on the automatic eye blink artifact elimination and the algorithm used for eye blink artifact detection is sample entropy. With these algorithms and the real-time processing flow we proposed, processing result of each EEG raw data is finished in 0.25 s after each sample time. In the end of this paper, the method used to evaluate the performance of eye blink artifact elimination is provided. Real EEG signals are also processed and the operation results are shown to remove the eye blink artifacts exactly without misses.