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 data set, which can uncover the data set'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 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 on the studied data set show that the proposed method obtains a difference of clusterization rate of 69% in Go vs. 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 clusterization rate. We also examine the effect of pre-processing 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.
- Interval features
- Possibilistic clustering