Epilepsy is marked by seizures stemming from abnormal electrical activity in the brain, causing involuntary movement or behavior. Many scientists have been working hard to explore the cause of epilepsy and seek the prevention and treatment. In the field of machine learning, epileptic diagnosis based on EEG signal has been a very hot research topic; many methods have been proposed, and considerable progress has been achieved. However, resorting the epileptic diagnosis techniques based on EEG to the reality applications still faces many challenges. Low signal-to-noise ratio (SNR) is one of the most important methodological challenges for EEG data collection and analysis. This paper discusses an automated diagnostic method for epileptic detection using a Fréchet Mean embedded in the Grassmann manifold analysis. Fréchet mean-based Grassmann discriminant analysis (FMGDA) algorithm to implement the EEG data dimensionality reduction and clustering task. The method is resorted to reduce Grassmann data from high-dimensional data to a relative lower-dimensional data and maximize between-class distance and minimize within-class distance simultaneously. Every EEG feature is mapped into the Grassmann manifold space first and then resort the Fréchet mean to represent the clustering center to carry out the clustering work. We designed a detailed experimental scheme to test the performance of our proposed algorithm; the test is assessed on several benchmark datasets. Experimental results have delivered that our approach leads to a significant improvement over state-of-the-art Grassmann manifold methods.

中文翻译：

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使用Grassmann判别分析方法在脑电图中进行癫痫检测。

癫痫病的特征是大脑中异常电活动引起的癫痫发作，引起不自主运动或行为。许多科学家一直在努力探索癫痫的原因并寻求预防和治疗。在机器学习领域，基于脑电信号的癫痫诊断一直是研究的热点。已经提出了许多方法，并且已经取得了很大的进展。然而，将基于脑电图的癫痫诊断技术应用于现实应用仍然面临许多挑战。低信噪比（SNR）是EEG数据收集和分析的最重要方法挑战之一。本文讨论了使用格拉斯曼流形分析中嵌入的FréchetMean进行癫痫发作检测的自动诊断方法。基于Fréchet均值的Grassmann判别分析（FMGDA）算法可实现EEG数据降维和聚类任务。该方法被采用来将Grassmann数据从高维数据减少到相对低维数据，并同时最大化类间距离和最小化类内距离。首先将每个EEG特征映射到Grassmann流形空间中，然后使用Fréchet均值表示聚类中心来执行聚类工作。我们设计了一个详细的实验方案来测试所提出算法的性能。该测试是在几个基准数据集上进行评估的。实验结果表明，与最先进的Grassmann流形方法相比，我们的方法带来了显着改进。