An electroencephalogram (EEG) based brain-computer interface (BCI) enables the control of some external activity directly from the brain, without any physical movement/overt action. The external activity can be the cursor control of a computer or it can provide commands to the devices to perform certain functions. This work proposes a movement imagery (MI) based graphical user interface (GUI) for typing 26 English alphabets and tasks like food, water, medicine along with cancel and confirm commands. Convolutional Neural Network (CNN) is used to extract the spatial features from the recorded EEG signals. These features are fed to an ensemble-based extreme gradient (XG) boost classifier in a five-classification framework. By varying the hyper-parameters of the classification model, the highest accuracy of 84.7% for CNN and 92.87% for the cascaded structure of CNN and the XG boost classifier is achieved. The minimum execution time taken is 1.18 s for CNN and 3.24 s using both CNN and XG boost classifier. The work shows that it is possible to classify the information embedded in MI signals and can serve as a basis for an alternate communication channel to patients in advanced stages of Amyotrophic lateral sclerosis.

基于脑电图（EEG）的脑机接口（BCI）可以直接从大脑控制某些外部活动，而无需任何身体运动/明显的动作。外部活动可以是计算机的光标控制，也可以向设备提供命令以执行某些功能。这项工作提出了一种基于运动图像（MI）的图形用户界面（GUI），用于键入26个英文字母和任务（如食物，水，药品）以及取消和确认命令。卷积神经网络（CNN）用于从记录的EEG信号中提取空间特征。将这些功能馈送到五分类框架中的基于集合的极端梯度（XG）提升分类器。通过更改分类模型的超参数，CNN和92的最高准确度为84.7％。CNN和XG Boost分类器的级联结构可达到87％。对于CNN，使用CNN和XG Boost分类器的最短执行时间为1.18 s，而最短执行时间为3.24 s。这项工作表明，可以对嵌入在MI信号中的信息进行分类，并可以作为向肌萎缩性侧索硬化晚期患者提供另一种交流渠道的基础。