Fuzzy Clustering for Exploratory Analysis of EEG Event-Related Potentials

Abstract

We introduce an analysis method for electroencephalography (EEG) data, focused on event-related potentials (ERPs). Our approach is unsupervised and makes use of a fuzzy clustering algorithm based on the possibilistic framework and includes a data-driven noise and artifact rejection phase. Our contribution provides a general analysis tool, applicable to any ERP dataset, which can uncover the dataset's internal structure. The fuzzy clustering algorithm is the core of our method, since its fine-grained membership grades how much a sample belongs to a given cluster, making the method applicable even when groups have a certain overlap. Prior to the clustering step, we apply weights to the feature vectors, optimizing them in order to enhance the variance within the dataset, and we extract time-window-interval-based features inspired by interval arithmetic. We apply the data processing workflow to the analysis of a set of ERPs recorded during an emotional Go/NoGo task. We evaluate the performance of the unsupervised analysis by computing a measure based on the clusterization rate of trials in different experimental conditions. The results of the studied dataset show that the proposed method obtains a difference of clusterization rate of 69% in Go versus NoGo trials, when weights and interval features are applied to the data, improving previous work not including weights and interval features, which had a rate of 31%. Furthermore, when compared with the standard fuzzy c-means, our proposed possibilistic clustering algorithm outperforms it in terms of the clusterization rate. We also examine the effect of preprocessing the data with independent component analysis and removing noise-related components and observe that this does not improve significantly the obtained results. These findings demonstrate that our proposed method provides a valuable data processing workflow robust to EEG artifacts and able to produce a clustering that is coherent with the experimental conditions represented in the ERP dataset.