Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient.

颅内脑电图（iEEG）和功能磁共振成像（fMRI）信号的模式分类已进一步促进了癫痫研究，以了解癫痫发作的起源并定位功能障碍的脑组织进行治疗。先前的研究表明，与传统的特征选择方法相比，使用遗传编程（GP）算法隐式选择特征可以更有效地确定适当的特征，以区分生物标志物和非生物标志物间质iEEG和fMRI活动。但是，对于每种iEEG和fMRI模式，仍不确定具有GP产生的间接特征选择的随机属性（a）患者数据集中的一致结果，以及（b）在多个患者数据集中特定或通用的特征。我们通过执行多次选择试验以及随后针对独立的iEEG和fMRI癫痫患者数据的频繁项集挖掘（FIM），使用GP算法检查了隐式选择特征以分类间质活动的可重复性。我们观察到受试者之间的一致性和受试者间的差异性，其中所选特征有些相似，这表明对患者特定特征的明确需求以及对患者特定特征选择或分类的可能需求。对于功能磁共振成像，使用最近邻分类和30个GP世代，我们获得了超过60％的中位灵敏度和超过60％的中位选择性。对于iEEG，使用最近邻分类法和30个GP代，除一名患者外，我们获得了超过65％的中位敏感性和超过65％的中位选择性。