论文标题
STN：一种从大脑活动模式中识别刺激类别的新张量网络方法
STN: a new tensor network method to identify stimulus category from brain activity pattern
论文作者
论文摘要
神经解码仍然是神经计算科学中的挑战和热门话题。最近，许多研究表明，大脑网络模式包含丰富的空间和时间结构信息，这代表了外部刺激下的大脑的激活信息。 ％因此，对大脑网络解码刺激的研究受到了广泛的关注。传统方法直接从通用机器学习方法中提取大脑网络特征，然后将这些功能放入分类器中，并意识到解码外部刺激。但是，该方法无法有效提取隐藏在大脑网络中的多维结构信息。张量研究人员表明，张量分解模型可以在多维结构数据中完全挖掘独特的时空结构特征。这项研究提出了刺激受限的张量脑模型（STN），其中涉及张量分解想法和刺激类别约束信息。该模型在实际神经影像学数据集（MEG和fMRI）上进行了验证。实验结果表明，与两个模态数据集上的其他方法相比，STN模型的精度矩阵获得了超过11.06％和18.46％。这些结果意味着提取有关STN模型的歧视性特征的优势，尤其是用于用语义信息解码对象刺激的优势。
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.
