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首页> 外文会议>International conference on biomagnetism advances in biomagnetism >Cluster-Based Algorithm for ROI Analysis and Cognitive State Decoding Using Single-Trial Source MEG Data
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Cluster-Based Algorithm for ROI Analysis and Cognitive State Decoding Using Single-Trial Source MEG Data

机译：基于群集的ROI分析和认知状态解码的算法使用单试源MEG数据进行解码

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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：我们介绍了一种基于群集的解码算法，用于从源空间（或最佳源集群）中的EEG / MEG数据中发现感兴趣区域（ROI），并在单个实验试验中预测多个条件。我们的算法自动识别了从个别脑源的假设试验中产生最大均值测试统计的邻接脑区域。我们表明，通过利用这些感兴趣的来源，可以使用单试性能通过贝叶斯解码预测实验条件。此外，我们通过以顺序时间间隔运行我们的算法，检查这些源的如何在大脑表面上在大脑表面上在空间和时间上演变。我们在视觉引导的手腕运动任务中展示了我们的方法，其中目标是推断腕部运动的方向。使用Elekta Neuomag记录MEG数据？当受试者在4个方向上执行了核心腕部移动的设备，并且使用最小规范估计方法从原始数据估算源极。我们的结果表明，该算法是使用P值的传统ROI分析的潜在替代品，成功发现与任务相关的最佳源集群，并在休假交叉验证中实现了显着的解码精度。

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