Brain-computer interface (BCI) assists communication for the disabled and handicapped. It is usually electroencephalogram (EEG) based and uses motor imagery (MI) in its operation. EEG signals are known for being nonstationary and are sensitive to artifacts from various sources such as the physical and mental state of the patient, their mood, their posture, and any external noise or distractions, etc. Processing of this data directly affects the classification accuracy, making it a critical step in any BCI system. Ensemble learning has been used for many kinds of BCI classification applications including MI and P300 event related potential, which has been proven to be robust. The purpose of this paper is to generate an algorithm that uses ensemble pruning method for EEG classification evoked by an MI task. In order to achieve this, we extracted the features of an EEG dataset and trained a range of support vector machines to make a diverse ensemble of classifiers. This ensemble is then pruned by using a novel optimization model by a difference of convex algorithm, which has not been used on EEG data before.

中文翻译：

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使用新颖的集成修剪方法对运动想象任务进行分类


脑机接口（BCI）协助残疾人士的沟通。它通常基于脑电图（EEG）并在其操作中使用运动想象（MI）。众所周知，脑电图信号是非平稳的，并且对各种来源的伪影敏感，例如患者的身体和精神状态、他们的情绪、他们的姿势以及任何外部噪音或干扰等。这些数据的处理直接影响分类的准确性，使其成为任何 BCI 系统中的关键一步。集成学习已用于多种 BCI 分类应用，包括 MI 和 P300 事件相关潜力，已被证明是稳健的。本文的目的是生成一种使用集成剪枝方法对 MI 任务引发的 EEG 分类的算法。为了实现这一目标，我们提取了脑电图数据集的特征并训练了一系列支持向量机来构建多样化的分类器集合。然后通过凸算法的差异使用新颖的优化模型对该集成进行修剪，该算法以前从未在脑电图数据上使用过。