论文标题
运动图像分类强调基于深度学习框架的相应频域方法
Motor Imagery Classification Emphasizing Corresponding Frequency Domain Method based on Deep Learning Framework
论文作者
论文摘要
脑电图是一种具有用户意图相关功能的非侵入性大脑信号，可在用户和计算机之间有效的双向途径。在这项工作中，我们提出了一个基于相应频率empahsize方法的深度学习框架，以解码2020年国际BCI竞争数据集的运动图像（MI）数据。 MI数据集由3类，即“圆柱”，“球形”和“ lumbrical”组成。我们利用功率谱密度作为强调方法和卷积神经网络来对修改后的MI数据进行分类。结果表明，MI相关的频率范围在MI任务过程中被激活，并提供了神经生理学的证据来设计所提出的方法。当使用建议的方法时，在会议内条件下的平均分类性能为69.68％，在会议间条件下的平均分类性能为52.76％。我们的结果提供了为实用应用开发基于BCI的设备控制系统的可能性。
The electroencephalogram, a type of non-invasive-based brain signal that has a user intention-related feature provides an efficient bidirectional pathway between user and computer. In this work, we proposed a deep learning framework based on corresponding frequency empahsize method to decode the motor imagery (MI) data from 2020 International BCI competition dataset. The MI dataset consists of 3-class, namely 'Cylindrical', 'Spherical', and 'Lumbrical'. We utilized power spectral density as an emphasize method and a convolutional neural network to classify the modified MI data. The results showed that MI-related frequency range was activated during MI task, and provide neurophysiological evidence to design the proposed method. When using the proposed method, the average classification performance in intra-session condition was 69.68% and the average classification performance in inter-session condition was 52.76%. Our results provided the possibility of developing a BCI-based device control system for practical applications.
