Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
PLoS One. 2010 Dec 30;5(12):e14465. doi: 10.1371/journal.pone.0014465.
Multivariate pattern analysis is a technique that allows the decoding of conceptual information such as the semantic category of a perceived object from neuroimaging data. Impressive single-trial classification results have been reported in studies that used fMRI. Here, we investigate the possibility to identify conceptual representations from event-related EEG based on the presentation of an object in different modalities: its spoken name, its visual representation and its written name. We used Bayesian logistic regression with a multivariate Laplace prior for classification. Marked differences in classification performance were observed for the tested modalities. Highest accuracies (89% correctly classified trials) were attained when classifying object drawings. In auditory and orthographical modalities, results were lower though still significant for some subjects. The employed classification method allowed for a precise temporal localization of the features that contributed to the performance of the classifier for three modalities. These findings could help to further understand the mechanisms underlying conceptual representations. The study also provides a first step towards the use of concept decoding in the context of real-time brain-computer interface applications.
多元模式分析是一种技术，它可以从神经影像学数据中解码出概念信息，例如感知物体的语义类别。在使用 fMRI 的研究中，已经报道了令人印象深刻的单次试验分类结果。在这里，我们研究了基于对象以不同模式呈现的可能性，即其口头名称、视觉表示和书面名称，从事件相关的 EEG 中识别概念表示。我们使用具有多元拉普拉斯先验的贝叶斯逻辑回归进行分类。对于测试的模式，分类性能存在明显差异。当对对象绘图进行分类时，获得了最高的准确性（89%的正确分类试验）。在听觉和拼写上，尽管对于某些对象来说结果较低，但仍然具有显著性。所采用的分类方法允许对有助于分类器性能的特征进行精确的时间定位，这对于三种模式都是如此。这些发现可以帮助进一步理解概念表示的机制。该研究还朝着在实时脑机接口应用中使用概念解码迈出了第一步。
