【摘要】 基于深度神经网络特征及恒河猴大脑视觉皮层脑电响应，提出了基于自适应信息融合方法的“脑在环路”图像表征模型，证明了脑机融合可以提供互补信息并提升深度神经网络模型性能；提出了基于大脑响应重建和共享表征空间的两种脑机融合计算模式，实现了“脑在环路建模，脑不在环路应用”，拓宽了脑机融合模型的应用场景；通过特征显著性可视化方法证明了共享表征有效性，为充分利用大脑视觉表征的神经响应提供了新思路。更多还原

【Abstract】 Based on the deep neural network features and EEG responses at the visual cortex of rhesus monkeys, an adaptive "brain-in-the-loop" fusion model of image representation is proposed, it is demonstrated that the brain-machine fusion can provide complementary information and enhance the performance of deep neural network model. Two kinds of brain-machine fusion computation mode based on brain response reconstruction and sharing representation space are proposed to accomplish the“brain-in-the-loop modeling and brain-out-of-the-loop application”, the application scenario of brain-machine fusion model is broadened. The effectiveness of sharing representation is proved by visualization method of feature saliency, a new thought is provided to make full use of neural response of brain visual representation.更多还原