Automated Machine Learning Framework for EEG/ERP Analysis: Viable Improvement on Traditional Approaches?

Abstract

Event Related Potential (ERP) measures derived from the electroencephalogram (EEG) have been widely used in research on language, cognition, and pathology. The high dimensionality (time x channel x condition) of a typical EEG/ERP dataset makes it a time-consuming prospect to properly analyze, explore, and validate knowledge without a particular restricted hypothesis. This study proposes an automated empirical greedy approach to the analysis process to datamine an EEG dataset for the location, robustness, and latency of ERPs, if any, present in a given dataset. We utilize Support Vector Machines (SVM), a well established machine learning model, on top of a preprocessing pipeline that focuses on detecting differences across experimental conditions. A hybrid of monte-carlo bootstrapping, cross-validation, and permutation tests is used to ensure the reproducibility of results. This framework serves to reduce researcher bias, time spent during analysis, and provide statistically sound results that are agnostic to dataset specifications including the ERPs in question. This method has been tested and validated on three different datasets with different ERPs (N100, Mismatch Negativity (MMN), N2b, Phonological Mapping Negativity (PMN), and P300). Results show statistically significant, above-chance level identification of all ERPs in their respective experimental conditions, latency, and location.