Fitzgibbon S P, Powers D M W, Pope K J, Clark C R
Cognitive Neuroscience Laboratory, School of Psychology, Flinders University, Adelaide, South Australia.
J Clin Neurophysiol. 2007 Jun;24(3):232-43. doi: 10.1097/WNP.0b013e3180556926.
A study was performed to investigate and compare the relative performance of blind signal separation (BSS) algorithms at separating common types of contamination from EEG. The study develops a novel framework for investigating and comparing the relative performance of BSS algorithms that incorporates a realistic EEG simulation with a known mixture of known signals and an objective performance metric. The key finding is that although BSS is an effective and powerful tool for separating and removing contamination from EEG, the quality of the separation is highly dependant on the type of contamination, the degree of contamination, and the choice of BSS algorithm. BSS appears to be most effective at separating muscle and blink contamination and less effective at saccadic and tracking contamination. For all types of contamination, principal components analysis is a strong performer when the contamination is greater in amplitude than the brain signal whereas other algorithms such as second-order blind inference and Infomax are generally better for specific types of contamination of lower amplitude.
开展了一项研究，以调查和比较盲信号分离（BSS）算法在从脑电图（EEG）中分离常见类型干扰方面的相对性能。该研究开发了一种新颖的框架，用于调查和比较BSS算法的相对性能，该框架将逼真的EEG模拟与已知信号的已知混合以及客观性能指标相结合。关键发现是，尽管BSS是从EEG中分离和去除干扰的有效且强大的工具，但分离质量高度依赖于干扰类型、干扰程度以及BSS算法的选择。BSS在分离肌肉和眨眼干扰方面似乎最有效，而在分离眼球跳动和追踪干扰方面效果较差。对于所有类型的干扰，当干扰幅度大于脑信号时，主成分分析表现出色，而其他算法，如二阶盲推断和信息最大化，通常在较低幅度的特定类型干扰方面表现更好。
