Mullen Tim R, Kothe Christian A E, Chi Yu Mike, Ojeda Alejandro, Kerth Trevor, Makeig Scott, Jung Tzyy-Ping, Cauwenberghs Gert
IEEE Trans Biomed Eng. 2015 Nov;62(11):2553-67. doi: 10.1109/TBME.2015.2481482. Epub 2015 Sep 23.
We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification.
The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system.
Simulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA (0.74 ±0.09) and LCMV (0.72 ±0.08) source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA (0.74 ±0.16) but significantly better for LCMV (0.82 ±0.12) .
We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG.
This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain-computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.
我们展示并评估一种可穿戴式高密度干电极脑电图（EEG）系统以及用于在线神经成像和状态分类的开源软件框架。
该系统集成了一个64通道的干电极EEG外形规格，并具备无线数据流以进行在线分析。应用了一个实时软件框架，包括自适应伪迹去除、皮质源定位、多变量有效连接性推断、数据可视化以及使用约束逻辑回归方法（ProxConn）从连接性特征进行认知状态分类。我们在模拟的64通道EEG数据上评估系统识别方法。然后，我们使用ProxConn和一种基准ERP方法，在九名受试者中使用干电极EEG系统对反应错误进行分类，以此评估系统性能。
模拟对于实时皮质连接性估计产生了高精度（曲线下面积[AUC]=0.97±0.021）。使用皮质有效连接性[短时直接定向传递函数（sdDTF）]进行反应错误分类显著高于随机水平，对于cLORETA（0.74±0.09）和线性约束最小方差（LCMV）（0.72±0.08）源定位具有相似的性能（AUC）。基于皮质ERP的分类对于cLORETA（0.74±0.16）与ProxConn相当，但对于LCMV（0.82±0.12）显著更好。
我们证明了从高密度可穿戴干电极EEG进行实时皮质连接性分析和认知状态分类的可行性。
本文是这些方法首次在64通道干电极EEG上的有效应用。本研究满足了在可穿戴环境的动态条件下对复杂脑活动进行稳健实时测量和解读的需求。此类进展可在研究、医学和脑机接口方面产生广泛影响。这些流程在开源的SIFT和BCILAB工具箱中可免费获取。
