Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG.
Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG.
复制标题
DOI：
10.1109/tbme.2015.2481482
复制
发表时间：
2015-11
期刊：
影响因子：
--
通讯作者：
Cauwenberghs G
中科院分区：
文献类型：
--
作者：
Mullen TR;Kothe CA;Chi YM;Ojeda A;Kerth T;Makeig S;Jung TP;Cauwenberghs G
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 9 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 (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. The work 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.
