论文标题
使用卷积神经网络对视觉感知和基于图像的EEG信号进行分类
Classification of Visual Perception and Imagery based EEG Signals Using Convolutional Neural Networks
论文作者
论文摘要
最近，在几个脑部计算机界面（BCI）研究中研究了视觉感知（VP）和视觉图像（VI）范例。 VP和VI分别在感知和记忆视觉信息时定义为大脑信号的变化。这些范式可以是先前基于视觉的范例的替代方案，这些范例具有限制，例如疲劳和低信息传输速率（ITR）。在这项研究中，我们分析了VP和VI，以研究控制BCI的可能性。首先，我们对与事件相关的光谱扰动进行了时频分析。此外，使用卷积神经网络获得了两种类型的解码精度，以验证是否可以将大脑信号与VP中的每个类别区分开，以及它们是否可以通过VP和VI范式区分。结果，VP中的6级分类性能为32.56％，分类两个范式的二进制分类性能为90.16％。
Recently, visual perception (VP) and visual imagery (VI) paradigms are investigated in several brain-computer interface (BCI) studies. VP and VI are defined as a changing of brain signals when perceiving and memorizing visual information, respectively. These paradigms could be alternatives to the previous visual-based paradigms which have limitations such as fatigue and low information transfer rates (ITR). In this study, we analyzed VP and VI to investigate the possibility to control BCI. First, we conducted a time-frequency analysis with event-related spectral perturbation. In addition, two types of decoding accuracies were obtained with convolutional neural network to verify whether the brain signals can be distinguished from each class in the VP and whether they can be differentiated with VP and VI paradigms. As a result, the 6-class classification performance in VP was 32.56% and the binary classification performance which classifies two paradigms was 90.16%.
