Yu Haitao, Zhao Quanfa, Li Shanshan, Li Kai, Liu Chen, Wang Jiang
School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Front Comput Neurosci. 2022 Mar 11;16:852281. doi: 10.3389/fncom.2022.852281. eCollection 2022.
A crucial point in neuroscience is how to correctly decode cognitive information from brain dynamics for motion control and neural rehabilitation. However, due to the instability and high dimensions of electroencephalogram (EEG) recordings, it is difficult to directly obtain information from original data. Thus, in this work, we design visual experiments and propose a novel decoding method based on the neural manifold of cortical activity to find critical visual information. First, we studied four major frequency bands divided from EEG and found that the responses of the EEG alpha band (8-15 Hz) in the frontal and occipital lobes to visual stimuli occupy a prominent place. Besides, the essential features of EEG data in the alpha band are further mined two manifold learning methods. We connect temporally consecutive brain states in the distribution random adjacency embedded (t-SNE) map on the trial-by-trial level and find the brain state dynamics to form a cyclic manifold, with the different tasks forming distinct loops. Meanwhile, it is proved that the latent factors of brain activities estimated by t-SNE can be used for more accurate decoding and the stable neural manifold is found. Taking the latent factors of the manifold as independent inputs, a fuzzy system-based Takagi-Sugeno-Kang model is established and further trained to identify visual EEG signals. The combination of t-SNE and fuzzy learning can highly improve the accuracy of visual cognitive decoding to 81.98%. Moreover, by optimizing the features, it is found that the combination of the frontal lobe, the parietal lobe, and the occipital lobe is the most effective factor for visual decoding with 83.05% accuracy. This work provides a potential tool for decoding visual EEG signals with the help of low-dimensional manifold dynamics, especially contributing to the brain-computer interface (BCI) control, brain function research, and neural rehabilitation.
神经科学中的一个关键点是如何从大脑动力学中正确解码认知信息，以用于运动控制和神经康复。然而，由于脑电图（EEG）记录的不稳定性和高维度，很难直接从原始数据中获取信息。因此，在这项工作中，我们设计了视觉实验，并提出了一种基于皮层活动神经流形的新型解码方法，以找到关键的视觉信息。首先，我们研究了从EEG划分出的四个主要频段，发现额叶和枕叶中EEG阿尔法频段（8-15赫兹）对视觉刺激的反应占据显著地位。此外，通过两种流形学习方法进一步挖掘了阿尔法频段中EEG数据的基本特征。我们在逐次试验水平上连接分布随机邻接嵌入（t-SNE）图中时间上连续的脑状态，发现脑状态动力学形成一个循环流形，不同任务形成不同的环。同时，证明了t-SNE估计的脑活动潜在因素可用于更准确的解码，并找到了稳定的神经流形。以流形的潜在因素作为独立输入，建立了基于模糊系统的高木-菅野-康模型，并进一步训练以识别视觉EEG信号。t-SNE和模糊学习的结合可以将视觉认知解码的准确率大幅提高到81.98%。此外，通过优化特征，发现额叶、顶叶和枕叶的组合是视觉解码最有效的因素，准确率为83.05%。这项工作借助低维流形动力学为解码视觉EEG信号提供了一个潜在工具，尤其有助于脑机接口（BCI）控制、脑功能研究和神经康复。
