Real time cortical connectivity imaging from EEG/MEG data

描述(由申请人提供)： 人脑的无创表面脑电(EEC)或脑磁图(MEG)的神经反馈为控制脑机接口(BCI)或治疗癫痫和注意缺陷多动障碍(ADHD)等疾病提供了重要的机制。它还可以研究人脑的功能方面，这在离线研究中是不可能观察到的，也就是那些只有在反馈过程中才被激活的神经元电路。传统上，反馈通道一直是记录在头皮表面的EEC电位或其频谱分量。由于体积导体效应，这些记录是对潜在皮质活动的模糊表示。这使得仅从通道信息来评估针对特定皮质区域的反馈是困难的。此外，原始EEC的低信噪比(SNR)是BCI或反馈应用中的限制因素。逆模型结合了个体受试者的几何形状，允许直接对皮质区域进行成像，并可以为这些区域提供特定的功能反馈。将逆方法与诸如相干、高阶谱分析(HOSA)、锁相或多变量自回归(MVAR)模型等连通性度量的应用相结合，将在两个方面增强神经反馈应用：由于噪声导致的区域间随机交互的概率将低于单通道度量；以及通过逆方法使用先验空间信息将利用所有可用通道，从而提高系统的信噪比和特异度。非线性方法，如HOSA，具有对高斯白噪声和线性串扰效应的健壮性，这可能会混淆连通性测量。基于皮质同步性测量的实时系统可用于训练受试者主动增加或减少选定皮质区域之间的同步性，并可促进ADHD或癫痫的神经反馈治疗。此外，大脑皮层同步性检测也可以直接应用于脑-机接口。我们将开发一种用于神经反馈应用的EEG/MEG皮质成像系统，该系统将实时成像选定区域之间的皮质同步。我们将通过对健康受试者的反馈实验来验证该系统，目的是使受试者能够使用所建议的成像系统来增加或减少选定区域的皮质同步性。

DESCRIPTION (provided by applicant): Neurofeedback by means of non-invasive surface Electroencephalography (EEC) or Magnetoencephalography (MEG) of the human brain provides an important mechanism for control of brain- computer-interfaces (BCI) or potential treatment for disorders such as epilepsy and attention deficit hyperactivity disorder (ADHD). It also allows studying functional aspects of the human brain, which are impossible to observe in off-line studies, i.e. those neuronal circuits, which only become activated during feedback. Traditionally, the feedback channels have been the EEC potentials recorded on the scalp surface or their spectral components. Due to volume conductor effects these recordings are blurred representations of the underlying cortical activity. This makes it difficult to assess feedback for specific cortical regions from the channel information alone. Also, the low signal to noise ratio (SNR) of the raw EEC is a limiting factor in BCI or feedback applications. An inverse model, which incorporates the individual subject geometry allows direct imaging of cortical regions and can provide specific functional feedback for these regions. Using an inverse method in combination with application of connectivity measures, such as coherence, higher order spectral analysis (HOSA), phase-locking or a multivariate autoregressive (MVAR) model will enhance neurofeedback applications in two ways: The probability of random interaction between regions due to noise will be lower than single channel measures and the use of a priori spatial information by an inverse method will utilize all avalable channels, thus increasing the SNR and specificity of the system. Non linear methods, such as HOSA, have the advantage of being robust against Gaussian white noise and linear crosstalk effects, which can confound connectivity measures. A real time system based on cortical synchrony measures can be used to train subjects to actively increase or decrease synchronization between selected cortical regions and can facilitate neurofeedback treatment of ADHD or epilepsy. Also, cortical synchrony detection can be directly applied to BCI. We will develop an EEG/MEG cortical imaging system for neurofeedback applications, which will image cortical synchrony between selected regions in real time. We will verify the system by feedback experiments on healthy subjects with the goal to enable subjects to increase or decrease cortical synchrony in selected regions using the proposed imaging system.