Centre for Mind/Brain Sciences, University of Trento, Rovereto, TN, Italy.
Brain Lang. 2011 Apr;117(1):12-22. doi: 10.1016/j.bandl.2010.09.013.
Achieving a clearer picture of categorial distinctions in the brain is essential for our understanding of the conceptual lexicon, but much more fine-grained investigations are required in order for this evidence to contribute to lexical research. Here we present a collection of advanced data-mining techniques that allows the category of individual concepts to be decoded from single trials of EEG data. Neural activity was recorded while participants silently named images of mammals and tools, and category could be detected in single trials with an accuracy well above chance, both when considering data from single participants, and when group-training across participants. By aggregating across all trials, single concepts could be correctly assigned to their category with an accuracy of 98%. The pattern of classifications made by the algorithm confirmed that the neural patterns identified are due to conceptual category, and not any of a series of processing-related confounds. The time intervals, frequency bands and scalp locations that proved most informative for prediction permit physiological interpretation: the widespread activation shortly after appearance of the stimulus (from 100 ms) is consistent both with accounts of multi-pass processing, and distributed representations of categories. These methods provide an alternative to fMRI for fine-grained, large-scale investigations of the conceptual lexicon.
实现对大脑范畴区分的更清晰认识对于我们理解概念词汇至关重要，但需要进行更精细的研究，以便这些证据能为词汇研究做出贡献。在这里，我们提出了一系列高级的数据挖掘技术，这些技术可以从 EEG 数据的单个试验中解码出单个概念的类别。当参与者默默地命名哺乳动物和工具的图像时，记录了神经活动，并且可以在单个试验中以高于偶然的准确性检测到类别，无论是考虑单个参与者的数据还是跨参与者的群体训练。通过对所有试验进行汇总，可以将单个概念以 98%的准确率正确分配到其类别中。算法做出的分类模式证实，所确定的神经模式是由于概念类别，而不是一系列与处理相关的混淆因素。对于预测最有信息的时间间隔、频带和头皮位置允许进行生理解释：刺激出现后不久（从 100 毫秒开始）广泛的激活与多通道处理的解释以及类别分布式表示一致。这些方法为概念词汇的精细、大规模研究提供了一种替代 fMRI 的方法。
