In the field of brain-computer interfaces, it is very common to use EEG signals for disease diagnosis. In this study, a style regularized least squares support vector machine based on multikernel learning is proposed and applied to the recognition of epilepsy abnormal signals. The algorithm uses the style conversion matrix to represent the style information contained in the sample, regularizes it in the objective function, optimizes the objective function through the commonly used alternative optimization method, and simultaneously updates the style conversion matrix and classifier during the iteration process parameter. In order to use the learned style information in the prediction process, two new rules are added to the traditional prediction method, and the style conversion matrix is used to standardize the sample style before classification.

中文翻译：

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通过多核学习的基于脑电图的癫痫识别

在脑机接口领域，使用EEG信号进行疾病诊断非常普遍。本研究提出了一种基于多核学习的正则化最小二乘支持向量机，并将其应用于癫痫异常信号的识别。该算法使用样式转换矩阵表示样本中包含的样式信息，将其规范化为目标函数，通过常用的替代优化方法对目标函数进行优化，并在迭代过程参数期间同时更新样式转换矩阵和分类器。为了在预测过程中使用学习到的样式信息，在传统的预测方法中添加了两个新规则，并且使用样式转换矩阵对分类之前的样本样式进行标准化。