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.

中文翻译：

---

基于运动图像的BCI系统中的零脑学习脑电分类

通过识别不同想象力任务的脑电图（EEG）模式，基于运动图像（MI）的脑机接口（BCI）将人的意图转换为计算机命令。但是，由于MI命令的缺乏和较长的校准时间，在实践中使用基于MI的BCI系统仍然具有挑战性。零散学习（ZSL）可以识别在训练过程中可能未见过其实例的物体，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图像任务，该任务是传统任务的组合，并提出了一种新颖的零脉冲学习模型，该模型可以识别已知和未知的EEG信号类别。这是通过首先学习从EEG特征到目标空间的非线性投影，然后应用新颖性检测方法将未知类与已知类区分开来实现的。从九个主题收集的数据集的应用证实了仅使用已经获得的运动图像数据来识别新型运动图像的可能性。结果表明，基于零镜头的方法的分类准确率占使用所有数据类别的传统方法的91.81％。