IEEE Trans Neural Syst Rehabil Eng. 2019 Apr;27(4):630-642. doi: 10.1109/TNSRE.2019.2904708. Epub 2019 Mar 13.
Electroencephalogram (EEG) signal recognition based on machine learning models is becoming more and more attractive in epilepsy detection. For multiclass epileptic EEG signal recognition tasks including the detection of epileptic EEG signals from different blends of different background data and epilepsy EEG data and the classification of different types of seizures, we may perhaps encounter two serious challenges: (1) a large amount of EEG signal data for training are not available and (2) the models for epileptic EEG signal recognition are often so complicated that they are not as easy to explain as a linear model. In this paper, we utilize the proposed transfer learning technique to circumvent the first challenge and then design a novel linear model to circumvent the second challenge. Concretely, we originally combine γ -LSR with transfer learning to propose a novel knowledge and label space inductive transfer learning model for multiclass EEG signal recognition. By transferring both knowledge and the proposed generalized label space from the source domain to the target domain, the proposed model achieves enhanced classification performance on the target domain without the use of kernel trick. In contrast to the other inductive transfer learning methods, the method uses the generalized linear model such that it becomes simpler and more interpretable. Experimental results indicate the effectiveness of the proposed method for multiclass epileptic EEG signal recognition.
基于机器学习模型的脑电图（EEG）信号识别在癫痫检测中越来越有吸引力。对于包括从不同背景数据和癫痫 EEG 数据的不同混合中检测癫痫 EEG 信号以及对不同类型的癫痫发作进行分类的多类癫痫 EEG 信号识别任务，我们可能会遇到两个严重的挑战：（1）用于训练的大量 EEG 信号数据不可用，（2）癫痫 EEG 信号识别的模型通常非常复杂，不如线性模型易于解释。在本文中，我们利用所提出的迁移学习技术来规避第一个挑战，然后设计一个新的线性模型来规避第二个挑战。具体来说，我们最初将 γ -LSR 与迁移学习相结合，提出了一种用于多类 EEG 信号识别的新的知识和标签空间归纳迁移学习模型。通过从源域向目标域转移知识和所提出的广义标签空间，所提出的模型在不使用核技巧的情况下在目标域上实现了增强的分类性能。与其他归纳迁移学习方法相比，该方法使用广义线性模型，使其更简单且更具可解释性。实验结果表明，该方法在多类癫痫 EEG 信号识别中的有效性。
