Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands.
Neuroimage. 2012 Feb 15;59(4):3641-51. doi: 10.1016/j.neuroimage.2011.11.056. Epub 2011 Nov 30.
Pattern recognition algorithms are becoming increasingly used in functional neuroimaging. These algorithms exploit information contained in temporal, spatial, or spatio-temporal patterns of independent variables (features) to detect subtle but reliable differences between brain responses to external stimuli or internal brain states. When applied to the analysis of electroencephalography (EEG) or magnetoencephalography (MEG) data, a choice needs to be made on how the input features to the algorithm are obtained from the signal amplitudes measured at the various channels. In this article, we consider six types of pattern analyses deriving from the combination of three types of feature selection in the temporal domain (predefined windows, shifting window, whole trial) with two approaches to handle the channel dimension (channel wise, multi-channel). We combined these different types of analyses with a Gaussian Naïve Bayes classifier and analyzed a multi-subject EEG data set from a study aimed at understanding the task dependence of the cortical mechanisms for encoding speaker's identity and speech content (vowels) from short speech utterances (Bonte, Valente, & Formisano, 2009). Outcomes of the analyses showed that different grouping of available features helps highlighting complementary (i.e. temporal, topographic) aspects of information content in the data. A shifting window/multi-channel approach proved especially valuable in tracing both the early build up of neural information reflecting speaker or vowel identity and the late and task-dependent maintenance of relevant information reflecting the performance of a working memory task. Because it exploits the high temporal resolution of EEG (and MEG), such a shifting window approach with sequential multi-channel classifications seems the most appropriate choice for tracing the temporal profile of neural information processing.
模式识别算法在功能神经影像学中得到了越来越广泛的应用。这些算法利用独立变量（特征）的时间、空间或时空模式中包含的信息，来检测大脑对外界刺激或内部状态的反应之间微妙但可靠的差异。当应用于脑电图（EEG）或脑磁图（MEG）数据的分析时，需要选择从在各个通道测量的信号幅度中获取算法输入特征的方法。在本文中，我们考虑了六种源自时域中三种特征选择类型（预定义窗口、滑动窗口、整个试验）与两种处理通道维度的方法（通道-wise、多通道）组合的模式分析类型。我们将这些不同类型的分析与高斯朴素贝叶斯分类器相结合，并分析了来自一项旨在理解编码说话者身份和言语内容（元音）的皮质机制的任务依赖性的多主体 EEG 数据集（Bonte、Valente 和 Formisano，2009）。分析的结果表明，可用特征的不同分组有助于突出数据中信息内容的互补（即时间、地形）方面。滑动窗口/多通道方法在追踪反映说话者或元音身份的早期神经信息的建立和反映工作记忆任务表现的后期和任务相关的相关信息的维持方面特别有价值。由于它利用了 EEG（和 MEG）的高时间分辨率，因此这种带有顺序多通道分类的滑动窗口方法似乎是追踪神经信息处理的时间分布的最合适选择。
