Department of Mathematical Information Technology, University of Jyväskylä, PL 35 (Agora), Jyväskylä 40014, Finland.
Int J Neural Syst. 2010 Aug;20(4):279-92. doi: 10.1142/S0129065710002413.
Independent component analysis (ICA) does not follow the superposition rule. This motivates us to study a negative event-related potential - mismatch negativity (MMN) estimated by the single-trial based ICA (sICA) and averaged trace based ICA (aICA), respectively. To sICA, an optimal digital filter (ODF) was used to remove low-frequency noise. As a result, this study demonstrates that the performance of the sICA+ODF and aICA could be different. Moreover, MMN under sICA+ODF fits better with the theoretical expectation, i.e., larger deviant elicits larger MMN peak amplitude.
独立成分分析(ICA)不遵循叠加规则。这促使我们研究由基于单试的独立成分分析(sICA)和基于平均迹的独立成分分析(aICA)分别估计的负事件相关电位-失匹配负波(MMN)。对于 sICA,使用最佳数字滤波器(ODF)来去除低频噪声。结果表明,sICA+ODF 和 aICA 的性能可能不同。此外,sICA+ODF 下的 MMN 更符合理论预期,即较大的偏差引发较大的 MMN 峰幅度。