Most intelligent algorithms used for recognizing epilepsy electroencephalogram (EEG) have two major deficiencies. The one is the lack of interpretability and the other is unsatisfactory recognition results. In response to these challenges, we propose a dedicated model called multi-view Takagi–Sugeno–Kang (TSK) fuzzy system (MV-TSK-FS) for the epilepsy EEG detection. Our contributions lie in three aspects. First, TSK-FS is selected as the basic model. As one of the most famous fuzzy systems, TSK-FS has the advantage of nice interpretability and thus meets the requirement of clinic trials and applications. Second, MV-TSK-FS uses a multi-view framework to collaboratively handle the collective feature data extracted from diverse extraction perspectives, which strives to avoid the potential performance degradation commonly incurred with single feature extraction. Third, we propose a view-weighted mechanism based on the quadratic regularization to distinguish the importance of each view. The more important the view, the larger the corresponding weight is. The final decision is consequently figured out with the weighted outputs of all views. Experimental results demonstrate that, compared with other epilepsy EEG detection ones, our proposed method has better classification performance as well as more satisfied interpretability on results.

用于识别癫痫脑电图（EEG）的大多数智能算法都有两个主要缺陷。一个是缺乏可解释性，另一个是令人满意的识别结果。为了应对这些挑战，我们提出了一种专用模型，称为癫痫脑电图检测的多视图高木-Sugeno-Kang（TSK）模糊系统（MV-TSK-FS）。我们的贡献来自三个方面。首先，选择TSK-FS作为基本模型。作为最著名的模糊系统之一，TSK-FS具有良好的可解释性的优势，因此可以满足临床试验和应用的要求。其次，MV-TSK-FS使用多视图框架来协作处理从不同提取角度提取的集体特征数据，努力避免单特征提取通常引起的潜在性能下降。第三，我们提出一种基于二次正则化的视图加权机制，以区分每个视图的重要性。视图越重要，相应的权重就越大。因此，最终决定是通过所有视图的加权输出得出的。实验结果表明，与其他癫痫脑电图检测方法相比，我们提出的方法具有更好的分类性能和更令人满意的结果解释性。因此，最终决定是通过所有视图的加权输出得出的。实验结果表明，与其他癫痫脑电图检测方法相比，我们提出的方法具有更好的分类性能和更令人满意的结果解释性。因此，最终决定是通过所有视图的加权输出得出的。实验结果表明，与其他癫痫脑电图检测方法相比，我们提出的方法具有更好的分类性能和更令人满意的结果解释性。