Neural decoding is still a challenge and hot topic in neurocomputing science. Recently, many studies have shown that brain network patterns containing rich spatial and temporal structure information, which represents the activation information of brain under external stimuli. %Therefore, the research of decoding stimuli from brain network received extensive more attention. The traditional method extracts brain network features directly from the common machine learning method, then puts these features into the classifier, and realizes to decode external stimuli. However, this method cannot effectively extract the multi-dimensional structural information, which is hidden in the brain network. The tensor researchers show that the tensor decomposition model can fully mine unique spatio-temporal structure characteristics in multi-dimensional structure data. This research proposed a stimulus constrained tensor brain model(STN)which involves the tensor decomposition idea and stimulus category constraint information. The model was verified on the real neuroimaging data sets (MEG and fMRI). The experimental results show that the STN model achieves more than 11.06% and 18.46% on accuracy matrix compared with others methods on two modal data sets. These results imply the superiority of extracting discriminative characteristics about STN model, especially for decoding object stimuli with semantic information.
翻译:神经解码仍然是神经科学中的一个挑战和热题。 最近, 许多研究显示, 包含丰富的空间和时间结构信息的大脑网络模式显示, 包含丰富的空间和时间结构信息的大脑网络模式在外部刺激下代表了大脑的激活信息。% 因此, 大脑网络解码刺激研究得到了广泛的关注。 传统方法直接从普通机器学习方法中提取大脑网络特征, 然后将这些特征引入分类器, 并实现外部模拟数据解码。 但是, 这个方法无法有效地提取大脑网络隐藏的多维结构信息。 高压解构模型可以完全覆盖多维结构数据中独特的神经结构特征。 这项研究提出了一种刺激抑制了脑结构模型( STN), 其中包括高压解析想法和刺激类别限制信息。 该模型在真实神经成像数据集( MEG 和 FMRI) 上被验证。 实验结果表明, STN 模型在精确度矩阵中取得了超过11.06 % 和 18.46 %, 与两个模型相比, 这些模型显示, 具有两个磁性数据模型的Sdelimal 的Slimality结果, 等的Slim 等数据模型将意味着Slim 的Slimbetimmexmexmexmexmex expecial resmexpecial 。