BACKGROUND As a physiological signal, EEG data cannot be subjectively changed or hidden. Compared with other physiological signals, EEG signals are directly related to human cortical activities with excellent temporal resolution. After the rapid development of machine learning and artificial intelligence, the analysis and calculation of EEGs has made great progress, leading to a significant boost in performances for content understanding and pattern recognition of brain activities across the areas of both neural science and computer vision. While such an enormous advance has attracted wide range of interests among relevant research communities, EEG-based classification of brain activities evoked by images still demands efforts for further improvement with respect to its accuracy, generalization, and interpretation, yet some characters of human brains have been relatively unexplored. METHODS We propose a region-level stacked bi-directional deep learning framework for EEG-based image classification. Inspired by the hemispheric lateralization of human brains, we propose to extract additional information at regional level to strengthen and emphasize the differences between two hemispheres. The stacked bi-directional long short-term memories are used to capture the dynamic correlations hidden from both the past and the future to the current state in EEG sequences. RESULTS Extensive experiments are carried out and our results demonstrate the effectiveness of our proposed framework. Compared with the existing state-of-the-arts, our framework achieves outstanding performances in EEG-based classification of brain activities evoked by images. In addition, we find that the signals of Gamma band are not only useful for achieving good performances for EEG-based image classification, but also play a significant role in capturing relationships between the neural activations and the specific emotional states. CONCLUSIONS Our proposed framework provides an improved solution for the problem that, given an image used to stimulate brain activities, we should be able to identify which class the stimuli image comes from by analyzing the EEG signals. The region-level information is extracted to preserve and emphasize the hemispheric lateralization for neural functions or cognitive processes of human brains. Further, stacked bi-directional LSTMs are used to capture the dynamic correlations hidden in EEG data. Extensive experiments on standard EEG-based image classification dataset validate that our framework outperforms the existing state-of-the-arts under various contexts and experimental setups.

中文翻译：

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通过区域级堆叠双向深度学习框架进行基于脑电图的图像分类。

背景技术脑电数据作为一种生理信号，无法被主观改变或隐藏。与其他生理信号相比，脑电信号与人类皮质活动直接相关，具有优异的时间分辨率。随着机器学习和人工智能的快速发展，脑电图的分析和计算取得了长足的进步，使得神经科学和计算机视觉领域的大脑活动内容理解和模式识别性能显着提升。虽然如此巨大的进步引起了相关研究界的广泛兴趣，但基于脑电图的图像引发的大脑活动分类在其准确性、泛化性和解释性方面仍需要进一步改进，但人类大脑的一些特征还需要进一步改进。相对未被探索。方法我们提出了一种用于基于脑电图的图像分类的区域级堆叠双向深度学习框架。受人类大脑半球偏侧化的启发，我们建议在区域层面提取额外的信息，以加强和强调两个半球之间的差异。堆叠式双向长短期记忆用于捕获脑电图序列中隐藏的过去和未来与当前状态的动态相关性。结果进行了大量的实验，我们的结果证明了我们提出的框架的有效性。与现有的最先进技术相比，我们的框架在基于脑电图的图像引起的大脑活动分类方面取得了出色的表现。此外，我们发现伽玛波段信号不仅有助于实现基于脑电图的图像分类的良好性能，而且在捕获神经激活和特定情绪状态之间的关系方面也发挥着重要作用。结论我们提出的框架为以下问题提供了一种改进的解决方案：给定用于刺激大脑活动的图像，我们应该能够通过分析脑电图信号来识别刺激图像来自哪一类。提取区域级信息是为了保留和强调人脑神经功能或认知过程的半球偏侧化。此外，堆叠双向 LSTM 用于捕获隐藏在脑电图数据中的动态相关性。对基于标准脑电图的图像分类数据集的广泛实验验证了我们的框架在各种背景和实验设置下优于现有的最先进技术。