Comparative Analysis of Functional Connectivity Metrics in EEG Datasets

Abstract

Analysis of functional connectivity helps to determine how brain regions interact with one another and to understand neurological diseases better. In this study, we compare functional connectivity networks derived from electroencephalogram (EEG) data using Pearson's correlation and mutual information. The TUH EEG Epilepsy Corpus (TUEP) dataset is analysed with methods from Graph Theory, Statistics and Machine Learning. Our findings can be used to develop features for predictive models. Specifically, we show that with just 19 channels, a convolutional neural network model achieves 94% and 95% area under the receiver operating characteristic (ROC) curve (AUC) for correlation and mutual information, respectively. Thus, we provide evidence that application of Machine Learning methods to EEG data not containing seizures can help to accurately identify individuals with epilepsy. This may have considerable implications on diagnosing the pathology.