School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
IEEE Trans Biomed Eng. 2011 Jun;58(6):1534-45. doi: 10.1109/TBME.2010.2090152. Epub 2010 Oct 28.
Event-related potentials (ERPs) reflect the brain activities related to specific behavioral events, and are obtained by averaging across many trial repetitions with individual trials aligned to the onset of a specific event, e.g., the onset of stimulus (s-aligned) or the onset of the behavioral response (r-aligned). However, the s-aligned and r-aligned ERP waveforms do not purely reflect, respectively, underlying stimulus (S-) or response (R-) component waveform, due to their cross-contaminations in the recorded ERP waveforms. Zhang [J. Neurosci. Methods, 80, pp. 49-63, 1998] proposed an algorithm to recover the pure S-component waveform and the pure R-component waveform from the s-aligned and r-aligned ERP average waveforms-however, due to the nature of this inverse problem, a direct solution is sensitive to noise that disproportionally affects low-frequency components, hindering the practical implementation of this algorithm. Here, we apply the Wiener deconvolution technique to deal with noise in input data, and investigate a Tikhonov regularization approach to obtain a stable solution that is robust against variances in the sampling of reaction-time distribution (when number of trials is low). Our method is demonstrated using data from a Go/NoGo experiment about image classification and recognition.
事件相关电位（ERPs）反映了与特定行为事件相关的大脑活动，通过对许多重复试验的平均处理得到，每个试验都与特定事件的起始时间对齐，例如刺激的起始时间（s 对齐）或行为反应的起始时间（r 对齐）。然而，由于记录的 ERP 波形中存在交叉污染，s 对齐和 r 对齐的 ERP 波形并不能纯粹反映潜在的刺激（S-）或反应（R-）分量波形。Zhang [J. Neurosci. Methods, 80, pp. 49-63, 1998]提出了一种从 s 对齐和 r 对齐的 ERP 平均波形中恢复纯 S 分量波形和纯 R 分量波形的算法——然而，由于这个逆问题的性质，直接解对噪声很敏感，会不成比例地影响低频分量，从而阻碍了该算法的实际应用。在这里，我们应用 Wiener 反卷积技术来处理输入数据中的噪声，并研究了一种 Tikhonov 正则化方法，以获得一种稳定的解，该解对反应时间分布采样的方差具有鲁棒性（当试验次数较少时）。我们的方法使用关于图像分类和识别的 Go/NoGo 实验的数据进行了演示。
