Cashero Zach, Anderson Chuck
Department of Computer Science, Colorado State University, Fort Collins, CO 80523, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7183-6. doi: 10.1109/IEMBS.2011.6091815.
This paper provides a comparison of several blind source separation (BSS) techniques as they are applied to EEG signals. Specifically, this work focuses on the P300 speller paradigm and assesses the classification accuracies for the identification of P300 trials. Previous work has shown that BSS methods such as independent component analysis (ICA) are useful in extracting the P300 source information from the background noise, increasing the classification rates. ICA will be compared with two other BSS methods, maximum noise fraction (MNF) and principal component analysis (PCA). In addition to this, we will analyze the effect of adding temporal information to the original data, which allows these BSS algorithms to find more complex spatio-temporal patterns.
本文对几种应用于脑电图（EEG）信号的盲源分离（BSS）技术进行了比较。具体而言，这项工作聚焦于P300拼写范式，并评估识别P300试验的分类准确率。先前的研究表明，诸如独立成分分析（ICA）等BSS方法有助于从背景噪声中提取P300源信息，提高分类率。ICA将与另外两种BSS方法，即最大噪声分量分析（MNF）和主成分分析（PCA）进行比较。除此之外，我们将分析向原始数据添加时间信息的效果，这能使这些BSS算法找到更复杂的时空模式。
