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Discovering and visualizing patterns in EEG data

机译:发现和可视化EEG数据中的模式

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Brain activity data is often collected through the use of electroencephalography (EEG). In this data acquisition modality, the electric fields generated by neurons are measured at the scalp. Although this technology is capable of measuring activity from a group of neurons, recent efforts provide evidence that these small neuronal collections communicate with other, distant assemblies in the brain's cortex. These collaborative neural assemblies are often found by examining the EEG record to find shared activity patterns. In this paper, we present a system that focuses on extracting and visualizing potential neural activity patterns directly from EEG data. Using our system, neuroscientists may investigate the spectral dynamics of signals generated by individual electrodes or groups of sensors. Additionally, users may interactively generate queries which are processed to reveal which areas of the brain may exhibit common activation patterns across time and frequency. The utility of this system is highlighted in a case study in which it is used to analyze EEG data collected during a working memory experiment.
机译:通常通过使用脑电图(EEG)收集大脑活动数据。在这种数据采集方式中,在头皮处测量神经元产生的电场。尽管该技术能够测量一组神经元的活动,但最近的努力提供了证据,表明这些小的神经元集合与大脑皮层中的其他远距离组件进行通信。通常通过检查EEG记录以找到共享的活动模式来找到这些协作神经程序集。在本文中,我们提出了一个专注于直接从EEG数据中提取和可视化潜在神经活动模式的系统。使用我们的系统,神经科学家可以研究由单个电极或传感器组生成的信号的频谱动力学。另外,用户可以交互地生成查询,这些查询被处理以揭示大脑的哪些区域可能在时间和频率上展现出共同的激活模式。在一个案例研究中强调了该系统的实用性,在该案例中,该系统用于分析在工作记忆实验期间收集的EEG数据。

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