A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification.
A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification.
复制标题
DOI：
10.1371/journal.pone.0135697
复制
发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Suppes P
中科院分区：
文献类型：
--
作者：
Kaneshiro B;Perreau Guimaraes M;Kim HS;Norcia AM;Suppes P
在日常生活中，物体类别的识别可以毫不费力地完成，但其神经基础仍然没有完全理解。在这项脑电图（EEG）研究中，我们使用单次试验分类进行表征相似性分析（RSA）的类别表示的对象在人类视觉皮层。当参与者观看一组72张具有计划类别结构的物体照片时，大脑反应被记录下来。用于RSA的代表性相异性矩阵（RDM）来自于对单个EEG试验进行操作的线性分类器的混淆。在过去的研究中，使用成对的相关性或分类来获得RDM，我们使用混淆矩阵从多类分类，它提供了新的自相似性措施，用于获得的代表性空间的整体大小。我们还对大脑反应的子集进行了分类，以确定最能区分对象类别和样本的空间和时间EEG成分。类别级分类的结果显示，大脑对人脸图像的反应形成了最明显的类别，而对两个无生命类别的图像的反应形成了一个单一的类别集群。示例级分类产生了大致相似的类别结构以及对应于自然语言类别的子集群。时空成分的大脑反应，区分一个类别内的样本被发现不同的类别之间的区分所涉及的。我们的研究结果表明，分类方法可以成功地应用于单次试验头皮记录EEG恢复细粒度的对象类别结构，以及识别可解释的时空组件的对象处理。最后，对象类别可以从记录在单个电极上的纯时间信息解码。
The recognition of object categories is effortlessly accomplished in everyday life, yet its neural underpinnings remain not fully understood. In this electroencephalography (EEG) study, we used single-trial classification to perform a Representational Similarity Analysis (RSA) of categorical representation of objects in human visual cortex. Brain responses were recorded while participants viewed a set of 72 photographs of objects with a planned category structure. The Representational Dissimilarity Matrix (RDM) used for RSA was derived from confusions of a linear classifier operating on single EEG trials. In contrast to past studies, which used pairwise correlation or classification to derive the RDM, we used confusion matrices from multi-class classifications, which provided novel self-similarity measures that were used to derive the overall size of the representational space. We additionally performed classifications on subsets of the brain response in order to identify spatial and temporal EEG components that best discriminated object categories and exemplars. Results from category-level classifications revealed that brain responses to images of human faces formed the most distinct category, while responses to images from the two inanimate categories formed a single category cluster. Exemplar-level classifications produced a broadly similar category structure, as well as sub-clusters corresponding to natural language categories. Spatiotemporal components of the brain response that differentiated exemplars within a category were found to differ from those implicated in differentiating between categories. Our results show that a classification approach can be successfully applied to single-trial scalp-recorded EEG to recover fine-grained object category structure, as well as to identify interpretable spatiotemporal components underlying object processing. Finally, object category can be decoded from purely temporal information recorded at single electrodes.