Performance of a Bayesian-Network-Model-Based BCI Using Single-Trial EEGs
Performance of a Bayesian-Network-Model-Based BCI Using Single-Trial EEGs
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DOI：
10.1587/transinf.2015edp7017
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
Maiko Sakamoto;Hiromi Yamaguchi;T. Yamazaki;K. Kamijo;T. Yamanoi
中科院分区：
文献类型：
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作者：
Maiko Sakamoto;Hiromi Yamaguchi;T. Yamazaki;K. Kamijo;T. Yamanoi
总结我们提出了一个新的贝叶斯网络模型（BNM）的框架，单次试验的EEG为基础的脑机接口（BCI）。BNM是在下面构建的。为了从运动想象任务中测量的单次EEG中区分待成像的左右手，BNM有以下三个步骤：（1）对每个单次EEG进行独立分量分析（伊卡）;（2）对每个IC在头皮表面的投影进行等效电流偶极子源定位（ECDL）;（3）使用ECDL结果构建BNM。BNM由分别对应于ECD所在的脑部位及其连接的节点和边缘组成。在每次试验中，通过概率推断计算的条件概率将连接量化为节点活动。将基于BNM的脑机接口与公共空间模式（CSP）方法进行了比较。对于10名健康受试者，两种方法之间无显著差异。我们的BNM可能会考虑每个主题的任务执行策略。
SUMMARY We have proposed a new Bayesian network model (BNM) framework for single-trial-EEG-based Brain-Computer Interface (BCI). The BNM was constructed in the following. In order to discriminate be-tween left and right hands to be imaged from single-trial EEGs measured during the movement imagery tasks, the BNM has the following three steps: (1) independent component analysis (ICA) for each of the single-trial EEGs; (2) equivalent current dipole source localization (ECDL) for projections of each IC on the scalp surface; (3) BNM construction using the ECDL results. The BNMs were composed of nodes and edges which correspond to the brain sites where ECDs are located, and their connections, respectively. The connections were quantified as node activities by conditional probabilities calculated by probabilistic inference in each trial. The BNM-based BCI is compared with the common spatial pattern (CSP) method. For ten healthy subjects, there was no significant di ff erence be-tween the two methods. Our BNM might reflect each subject’s strategy for task execution.