Decreasing the processor load to an acceptable level challenges researchers as an important threshold in the study of real-time detection and the prediction of epileptic seizures. The main methods in overcoming this problem are feature selection, dimension reduction, and electrode selection. This study is an evaluation of the performances of EEG signals, obtained from different channels in the detection processes of epileptic stages, in epileptic individuals. In particular, it aimed to analyze the separation levels of preictal periods from other periods and to evaluate the effects of the electrode selection on seizure prediction studies. The EEG signals belong to 14 epileptic patients. A feature set was formed for each patient using 20 features widely used in epilepsy studies. The number of features was decreased to 8 using principal component analysis. The reduced feature set was divided into testing and training data, using the cross-validation method. The testing data were classified with linear discriminant analysis and the results of the classification were evaluated individually for each patient and channel. Variability of up to 29.48 % was observed in the average of classification accuracy due to the selection of channels.

中文翻译：

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通道对癫痫患者发作前期检测的评价

将处理器负载降低到可接受的水平对研究人员提出了挑战，因为它是实时检测和癫痫发作预测研究中的一个重要阈值。克服这个问题的主要方法是特征选择、降维和电极选择。本研究是对癫痫个体在癫痫阶段检测过程中从不同通道获得的脑电信号的性能的评估。特别是，它旨在分析​​发作前期与其他时期的分离水平，并评估电极选择对癫痫发作预测研究的影响。EEG 信号属于 14 名癫痫患者。使用癫痫研究中广泛使用的 20 个特征为每位患者形成一个特征集。使用主成分分析将特征数量减少到 8 个。使用交叉验证方法将缩减的特征集划分为测试和训练数据。测试数据通过线性判别分析进行分类，并对每个患者和通道的分类结果进行单独评估。由于通道的选择，在分类准确度的平均值中观察到高达 29.48% 的变化。