Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy detection. Recently sparse representation-based classification (SRC) methods have achieved the good performance in EEG signal automatic detection, by which the EEG signals are sparsely represented using a few active coefficients in the dictionary and classified according to the reconstruction criteria. However, most of SRC learn a linear dictionary for encoding, and cannot extract enough information and nonlinear relationship of data for classification. To solve this problem, a hierarchical discriminative sparse representation classification model (called HD-SRC) for EEG signal detection is proposed. Based on the framework of neural network, HD-SRC learns the hierarchical nonlinear transformation and maps the signal data into the nonlinear transformed space. Through incorporating this idea into label consistent K singular value decomposition (LC-KSVD) at the top layer of neural network, HD-SRC seeks discriminative representation together with dictionary, while minimizing errors of classification, reconstruction and discriminative sparse-code for pattern classification. By learning the hierarchical feature mapping and discriminative dictionary simultaneously, more discriminative information of data can be exploited. In the experiment the proposed model is evaluated on the Bonn EEG database, and the results show it obtains satisfactory classification performance in multiple EEG signal detection tasks.

中文翻译：

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一种用于脑电信号检测的分层判别稀疏表示分类器

脑电图 (EEG) 信号数据的分类在癫痫检测中起着至关重要的作用。最近基于稀疏表示的分类（SRC）方法在脑电信号自动检测中取得了良好的性能，该方法使用字典中的一些活动系数对脑电信号进行稀疏表示，并根据重建标准进行分类。然而，大部分 SRC 学习线性字典进行编码，无法提取足够的信息和数据的非线性关系进行分类。为了解决这个问题，提出了一种用于EEG信号检测的分层判别稀疏表示分类模型（称为HD-SRC）。HD-SRC基于神经网络框架，学习层次非线性变换，将信号数据映射到非线性变换空间。通过将这一思想融入到神经网络顶层的标签一致 K 奇异值分解 (LC-KSVD) 中，HD-SRC 与字典一起寻求判别表示，同时最大限度地减少模式分类的分类、重构和判别稀疏码的错误。通过同时学习层次特征映射和判别字典，可以利用数据的更多判别信息。在实验中，该模型在波恩脑电数据库上进行了评估，结果表明它在多个脑电信号检测任务中获得了令人满意的分类性能。同时最小化模式分类的分类、重构和判别稀疏代码的错误。通过同时学习层次特征映射和判别字典，可以利用数据的更多判别信息。在实验中，该模型在波恩脑电数据库上进行了评估，结果表明它在多个脑电信号检测任务中获得了令人满意的分类性能。同时最小化模式分类的分类、重构和判别稀疏代码的错误。通过同时学习层次特征映射和判别字典，可以利用数据的更多判别信息。在实验中，该模型在波恩脑电数据库上进行了评估，结果表明它在多个脑电信号检测任务中获得了令人满意的分类性能。