Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information
Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information
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DOI：
10.1016/j.neuroimage.2013.12.035
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发表时间：
2014-04-15
期刊：
影响因子：
5.7
通讯作者：
Ishii, Shin
中科院分区：
文献类型：
--
作者：
Morioka, Hiroshi;Kanemura, Atsunori;Ishii, Shin
对于实际的脑机接口（BMI），脑电图（EEG）和近红外光谱（NIRS）是目前唯一的非侵入性方法，可在非实验室环境中使用。然而，EEG和NIRS的使用涉及某些固有的问题。EEG信号通常是来自广泛区域的神经活动的混合物，其中一些可能与BMI目标任务无关，因此损害BMI性能。NIRS测量血流量时具有固有的时间延迟，因此降低了实际的实时BMI效用。为了改善基于真实的环境脑电近红外光谱的脑机接口，我们提出了一种新的方法，其中被试的精神状态解码从脑电估计的皮层电流，从近红外光谱的信息的帮助下。使用变分贝叶斯多模态脑电描记术（VBMEG）的方法，我们纳入了一种新的形式的NIRS为基础的前捕获事件相关的去极化从孤立的电流源在皮层表面上。然后，我们应用贝叶斯逻辑回归技术从进一步稀疏化的电流源中解码受试者的精神状态。将我们的方法应用于空间注意力任务，我们发现我们的基于EEG-NIRS的解码器比仅基于EEG传感器信号的解码方法表现出显着的性能改善。我们方法的进步，从大脑皮层上稀疏孤立的电流源解码，也得到了神经科学考虑的支持;顶内沟，一个已知参与空间注意的区域，是我们任务中的关键负责区域。这些结果表明，我们的方法不仅是一个实用的选择EEG-NIRS为基础的BMI应用程序，但也是一个潜在的工具，在非实验室和自然环境中研究大脑活动。(C)2013 Elsevier Inc. All rights reserved.
For practical brain-machine interfaces (BMIs), electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are the only current methods that are non-invasive and available in non-laboratory environments. However, the use of EEG and NIRS involves certain inherent problems. EEG signals are generally a mixture of neural activity from broad areas, some of which may not be related to the task targeted by BMI, hence impairing BMI performance. NIRS has an inherent time delay as it measures blood flow, which therefore detracts from practical real-time BMI utility. To try to improve real environment EEG-NIRS-based BMIs, we propose here a novel methodology in which the subjects' mental states are decoded from cortical currents estimated from EEG, with the help of information from NIRS. Using a Variational Bayesian Multimodal EncephaloGraphy (VBMEG) methodology, we incorporated a novel form of NIRS-based prior to capture event related desynchronization from isolated current sources on the cortical surface. Then, we applied a Bayesian logistic regression technique to decode subjects' mental states from further sparsified current sources. Applying our methodology to a spatial attention task, we found our EEG-NIRS-based decoder exhibited significant performance improvement over decoding methods based on EEG sensor signals alone. The advancement of our methodology, decoding from current sources sparsely isolated on the cortex, was also supported by neuroscientific considerations; intraparietal sulcus, a region known to be involved in spatial attention, was a key responsible region in our task. These results suggest that our methodology is not only a practical option for EEG-NIRS-based BMI applications, but also a potential tool to investigate brain activity in non-laboratory and naturalistic environments. (C) 2013 Elsevier Inc. All rights reserved.