Predicting the recognition of natural scenes from single trial MEG recordings of brain activity
Predicting the recognition of natural scenes from single trial MEG recordings of brain activity
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DOI：
10.1016/j.neuroimage.2008.06.014
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发表时间：
2000-05
期刊：
影响因子：
5.7
通讯作者：
J. Rieger;C. Reichert;K. Gegenfurtner;T. Noesselt;C. Braun;H. Heinze;R. Kruse;H. Hinrichs
中科院分区：
文献类型：
--
作者：
J. Rieger;C. Reichert;K. Gegenfurtner;T. Noesselt;C. Braun;H. Heinze;R. Kruse;H. Hinrichs
在我们的日常生活中，我们会看到很多场景。有些人很快就被遗忘了，但有些人我们后来才认识到。我们准确地预测识别成功的自然场景照片使用单次试验脑磁图（MEG）的大脑激活措施。具体来说，我们证明，MEG响应在最初的600毫秒以下的发病现场照片允许预测准确率高达84.1%，使用线性支持向量机分类（SVM）。置换测试证实，所有基于LSVM的预测率均显著优于“猜测”。更一般地说，我们提出了四种使用lSVM分析大脑功能的方法。(1)我们表明，支持向量机可用于从脑磁图数据中提取大脑激活的时空模式。(2)我们表明，lSVM分类可以证明预测场景识别的相对早期和晚期过程之间的显着相关性，表明这些过程随着时间的推移之间的依赖性。(3)我们使用LSVM分类比较振荡和事件相关的脑磁图激活的信息内容，并显示它们包含类似的量和很大程度上重叠的信息。(4)对不同频带的单次试验预测性的更详细分析显示，5 Hz左右的θ频带活动允许最高的预测率，并且这些预测率与使用完整数据集获得的预测率无法区分。总之，我们的研究结果清楚地表明，支持向量机可以可靠地预测自然场景识别从单次试验脑磁图激活措施，可以是一个有用的工具，用于分析预测的大脑功能。
In our daily life we look at many scenes. Some are rapidly forgotten, but others we recognize later. We accurately predicted recognition success with natural scene photographs using single trial magnetoencephalography (MEG) measures of brain activation. Specifically, we demonstrate that MEG responses in the initial 600 ms following the onset of scene photographs allow for prediction accuracy rates up to 84.1% using linear Support-Vector-Machine classification (lSVM). A permutation test confirmed that all lSVM based prediction rates were significantly better than "guessing". More generally, we present four approaches to analyzing brain function using lSVMs. (1) We show that lSVMs can be used to extract spatio-temporal patterns of brain activation from MEG-data. (2) We show lSVM classification can demonstrate significant correlations between comparatively early and late processes predictive of scene recognition, indicating dependencies between these processes over time. (3) We use lSVM classification to compare the information content of oscillatory and event-related MEG-activations and show they contain a similar amount of and largely overlapping information. (4) A more detailed analysis of single-trial predictiveness of different frequency bands revealed that theta band activity around 5 Hz allowed for highest prediction rates, and these rates are indistinguishable from those obtained with a full dataset. In sum our results clearly demonstrate that lSVMs can reliably predict natural scene recognition from single trial MEG-activation measures and can be a useful tool for analyzing predictive brain function.