Goal

To apply signal processing and machine learning skills and knowledge in processing the EEG and MEG signal and further localize and evaluate the source of the finger stimulation.

Methods

Cognitive control is usually applied in information processing and behavioral response. In the preprocessing, baseline correction is implemented to analyze the pre-stimuli, combining ERP to mark the event related potential, studying the time-locked only behavior. Z-score transform, coherence and spec trum are calculated and analyzed in the functional connectivity analysis.

In addition to the functional analysis, Bayes Optimizer evaluates the neuro imaging according to the hierarchical Bayes. The introduction of the application is described from both user and developer’s prospects. Results: Introduction of both user and developers aspects, on its modules from pre-processing, functional analysis and results visualization and evaluation is conducted with one specific clinical data case, including the correlation is higher especially on gamma band and the MVAR coherence on the whole source space depicting the relation between different regions, especially on somatosensory (compared by thalamus) when stimulated by finger activity, phase-lock property of the E/MEG signal and etc. Compared to a manual selection, the scaling parameter prediction can be improved with support vector machine (SVM). The evaluation results with Bayes Optimization, location prediction is superior in the somatosensory area and in the thalamus, the total reconstructed source space is larger, one of the realization of cognitive system comparing different kernels and classifiers. The SVM and discriminant classifier gives similar results evaluating the dipole localization and the parameter choice related as well to the shape parameter, noise level, hyperprior and etc.

Conclusion

Approaches of Brain Q are found to be suitable for pre-processing for the EEG and MEG data. The system is capable of functional analysis including coherence and spectral related computation. Machine learning techniques are conducted as well to analyze and evaluate the result of the dipole reconstruction and help to predict the better model parameters and the localization of the origin dipoles. A case on finger stimulation clinical data is conducted and the results of the analysis temporarily and spatially manifests its functionality for users and potential extensions for developers.

中文翻译：

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基于机器学习和信号处理的扩展应用程序“Brain Q”处理从“Zeffiro”扩展而来的手指刺激的脑电图和 MEG 数据

目标

应用信号处理和机器学习技能和知识来处理 EEG 和 MEG 信号，并进一步定位和评估手指刺激的来源。

方法

认知控制通常应用于信息处理和行为反应。在预处理中，通过基线校正对预刺激进行分析，结合ERP标记事件相关电位，研究限时行为。在功能连通性分析中计算和分析 Z 分数变换、相干性和频谱。

除了功能分析之外，贝叶斯优化器还根据分层贝叶斯评估神经成像。应用程序的介绍是从用户和开发人员的前景来描述的。结果：引入用户和开发者两个方面，其从预处理、功能分析和结果可视化和评估的模块是针对一个特定的临床数据案例进行的，包括相关性更高，尤其是伽马波段和整体MVAR相干性源空间描绘了不同区域之间的关系，尤其是在手指活动、E/MEG 信号的锁相特性等刺激时的体感（与丘脑相比）。 与手动选择相比，缩放参数预测可以通过支持向量机 (SVM)。贝叶斯优化的评价结果​​，位置预测在体感区和丘脑区更优，总重构源空间更大，是比较不同核和分类器的认知系统的实现之一。SVM 和判别分类器给出了类似的结果，评估偶极子定位和与形状参数、噪声水平、超先验等相关的参数选择。

结论

发现 Brain Q 的方法适用于 EEG 和 MEG 数据的预处理。该系统能够进行功能分析，包括相干性和光谱相关计算。还进行了机器学习技术来分析和评估偶极子重建的结果，并有助于预测更好的模型参数和原偶极子的定位。进行了手指刺激临床数据的案例，分析结果临时和空间地向用户展示了其功能，并为开发人员提供了潜在的扩展。