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Cluster-Based Algorithm for ROI Analysis and Cognitive State Decoding Using Single-Trial Source MEG Data

机译:基于聚类的单次尝试MEG数据用于ROI分析和认知状态解码的算法

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We present a cluster-based decoding algorithm for discovering regions of interest (ROIs) from EEG/MEG data in source space (or optimal cluster of sources) and predicting multiple conditions in a single experimental trial. Our algorithm automatically identifies contiguous brain regions that yield maximum mean test statistics from hypothesis tests over individual brain sources. We show that by utilizing these sources of interest it is possible to predict the experimental conditions via Bayesian decoding using single trials. Furthermore, we examine how these sources evolve spatially and temporally over the brain surface by running our algorithm at sequential time intervals. We demonstrate our method in a visually-guided wrist movement task where the goal is to infer the direction of wrist movement. MEG data were recorded using an Elekta Neuromag® device while subjects performed center-out wrist movements in 4 directions, and sources were estimated from raw data using the Minimum Norm Estimate method |1|. Our results demonstrate that the algorithm is a potential alternative to traditional ROI analysis using p-values, successfully discovers optimal clusters of sources relevant to the task, and achieves significant decoding accuracies in leave-one-out cross validation.
机译:我们提出了一种基于簇的解码算法,用于从源空间(或最佳源簇)中的EEG / MEG数据中发现感兴趣区域(ROI),并在单个实验中预测多种情况。我们的算法会自动识别连续的大脑区域,这些区域从各个大脑来源的假设检验中得出最大的平均检验统计量。我们表明,通过利用这些感兴趣的资源,可以使用单个试验通过贝叶斯解码来预测实验条件。此外,我们通过以连续的时间间隔运行我们的算法,研究了这些来源在大脑表面上如何在空间和时间上演化。我们在视觉引导的腕部运动任务中演示了我们的方法,该任务的目的是推断腕部运动的方向。 MEG数据是使用ElektaNeuromag®设备记录的,而受试者在4个方向上进行了腕部外摆运动,并且使用最小范数估计方法| 1 |从原始数据中估计了来源。我们的结果表明,该算法是使用p值的传统ROI分析的一种潜在替代方法,可以成功发现与任务相关的最佳来源簇,并在留一法式交叉验证中获得显着的解码准确性。

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