论文标题
使用深度学习用于脑部计算机界面系统的EEG运动图像分类
Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems
论文作者
论文摘要
训练有素的T1类卷积神经网络（CNN）模型将用于检查其在饲养预处理的脑电图（EEG）数据时成功识别运动成像的能力。从理论上讲，如果对模型进行了准确的培训，则应该能够识别一个类并相应地标记。然后，将恢复CNN模型，并用于尝试使用较小的采样数据来识别相同类别的运动图像数据，以模拟实时数据。
A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data in an attempt to simulate live data.
