Zero-Shot Learning for EEG Classification in Motor Imagery-Based BCI System
Zero-Shot Learning for EEG Classification in Motor Imagery-Based BCI System
复制标题
基于运动想象的 BCI 系统中脑电图分类的零样本学习
DOI：
10.1109/tnsre.2020.3027004
复制
发表时间：
2020-11-01
影响因子：
4.9
通讯作者：
Zhuang, Jie
中科院分区：
文献类型：
--
作者：
Duan, Lili;Li, Jie;Zhuang, Jie
基于运动想象（MI）的脑机接口（BCI）通过识别不同想象任务的脑电图（EEG）模式，将人类意图转化为计算机命令。然而，由于MI命令的稀缺性和校准时间较长，在实践中使用基于MI的BCI系统仍然具有挑战性。零样本学习（ZSL）可以识别在训练期间可能未见过的实例的对象，有可能大大减少校准时间。因此，在这种情况下，我们首先尝试使用一种新型的运动想象任务，它是传统任务的组合，并提出了一种新颖的零样本学习模型，可以识别已知和未知类别的脑电图信号。这是通过首先学习从脑电图特征到目标空间的非线性投影，然后应用新颖性检测方法来区分未知类别和已知类别来实现的。对从九名受试者收集的数据集的应用证实了仅使用已获得的运动想象数据来识别新型运动想象的可能性。结果表明，我们基于零样本的方法的分类精度占使用所有类别数据的传统方法的 91.81%。
A brain-computer interface (BCI) based on motor imagery (MI) translates human intentions into computer commands by recognizing the electroencephalogram (EEG) patterns of different imagination tasks. However, due to the scarcity of MI commands and the long calibration time, using the MI-based BCI system in practice is still challenging. Zero-shot learning (ZSL), which can recognize objects whose instances may not have been seen during training, has the potential to substantially reduce the calibration time. Thus, in this context, we first try to use a new type of motor imagery task, which is a combination of traditional tasks and propose a novel zero-shot learning model that can recognize both known and unknown categories of EEG signals. This is achieved by first learning a non-linear projection from EEG features to the target space and then applying a novelty detection method to differentiate unknown classes from known classes. Applications to a dataset collected from nine subjects confirm the possibility of identifying a new type of motor imagery only using already obtained motor imagery data. Results indicate that the classification accuracy of our zero-shot based method accounts for 91.81% of the traditional method which uses all categories of data.