Hramov Alexander E, Maksimenko Vladimir A, Pchelintseva Svetlana V, Runnova Anastasiya E, Grubov Vadim V, Musatov Vyacheslav Yu, Zhuravlev Maksim O, Koronovskii Alexey A, Pisarchik Alexander N
REC "Artificial Intelligence Systems and Neurotechnology", Yuri Gagarin State Technical University of Saratov, Saratov, Russia.
Faculty of Nonlinear Processes, Saratov State University, Saratov, Russia.
Front Neurosci. 2017 Dec 4;11:674. doi: 10.3389/fnins.2017.00674. eCollection 2017.
In order to classify different human brain states related to visual perception of ambiguous images, we use an artificial neural network (ANN) to analyze multichannel EEG. The classifier built on the basis of a multilayer perceptron achieves up to 95% accuracy in classifying EEG patterns corresponding to two different interpretations of the Necker cube. The important feature of our classifier is that trained on one subject it can be used for the classification of EEG traces of other subjects. This result suggests the existence of common features in the EEG structure associated with distinct interpretations of bistable objects. We firmly believe that the significance of our results is not limited to visual perception of the Necker cube images; the proposed experimental approach and developed computational technique based on ANN can also be applied to study and classify different brain states using neurophysiological data recordings. This may give new directions for future research in the field of cognitive and pathological brain activity, and for the development of brain-computer interfaces.
为了对与模糊图像视觉感知相关的不同人类脑状态进行分类，我们使用人工神经网络（ANN）来分析多通道脑电图。基于多层感知器构建的分类器在对与内克尔立方体的两种不同解释相对应的脑电图模式进行分类时，准确率高达95%。我们分类器的重要特点是，在一个受试者上训练后，它可用于对其他受试者的脑电图轨迹进行分类。这一结果表明，与双稳态物体的不同解释相关的脑电图结构中存在共同特征。我们坚信，我们研究结果的意义不仅限于内克尔立方体图像的视觉感知；所提出的实验方法和基于人工神经网络开发的计算技术也可应用于利用神经生理数据记录来研究和分类不同的脑状态。这可能为认知和病理性脑活动领域的未来研究以及脑机接口的发展提供新的方向。
