发现和可视化脑电图数据模式

Erik W. Anderson, C. Chong, Gilbert Preston, Cláudio T. Silva
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引用次数: 1

摘要

脑活动数据通常是通过使用脑电图(EEG)收集的。在这种数据采集方式中,神经元产生的电场在头皮上被测量。虽然这项技术能够测量一组神经元的活动,但最近的研究提供了证据,证明这些小的神经元集合与大脑皮层中其他遥远的集合进行交流。通过检查脑电图记录来发现共享的活动模式,通常可以发现这些协同的神经组合。在本文中,我们提出了一个直接从脑电图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产生的信号的光谱动力学。此外,用户可以交互式地生成查询,这些查询被处理以揭示大脑的哪些区域可能在不同的时间和频率上表现出共同的激活模式。通过对工作记忆实验中采集的脑电数据进行分析,说明了该系统的实用性。
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Discovering and visualizing patterns in EEG data
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.
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