Decoding semantic relatedness and prediction from EEG: A classification method comparison

Machine-learning (ML) decoding methods have become a valuable tool for analyzing information represented in electroencephalogram (EEG) data. However, a systematic quantitative comparison of the performance of major ML classifiers for the decoding of EEG data in neuroscience studies of cognition is lacking. Using EEG data from two visual word-priming experiments examining well-established N400 effects of prediction and semantic relatedness, we compared the performance of three major ML classifiers that each use different algorithms: support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF). We separately assessed the performance of each classifier in each experiment using EEG data averaged over cross-validation blocks and using single-trial EEG data by comparing them with analyses of raw decoding accuracy, effect size, and feature importance weights. The results of these analyses demonstrated that SVM outperformed the other ML methods on all measures and in both experiments.

Automated summary

Machine-learning (ML) decoding methods have been found valuable in analyzing information represented in electroencephalogram (EEG) data. However, a systematic comparison of major ML classifiers for EEG decoding in neuroscience studies of cognition is lacking. This study compared three major ML classifiers, SVM, LDA, and RF, using EEG data from visual word-priming experiments. The results showed that SVM outperformed the other methods in both experiments and on all measures.