Individualized real‐time prediction of working memory performance by classifying electroencephalography signals

Abstract

AbstractAn individualized model that predicts trial‐by‐trial working memory performance is instrumental for personalized interventions. Here, we propose a single‐trial electroencephalography (EEG) classification process predicting individuals' responses, that is, target correct versus target non‐correct during a working memory task, N‐back. We used event‐related (de‐)synchronization (ERD and ERS) prior to an anticipatory cue as features. The proposed comprehensive process addresses single‐trial EEG classification challenges such as temporal overlap between training and testing datasets, feature selection's stability, and significance of the classification accuracy which have been often overlooked. Our model identified for the first time a few (ranged between 4 and 10) brain regions and oscillations where ERD and ERS predicted an individual's performance. Mean (SD) prediction accuracy across 50 participants (mean age [SD] = 28.56 [7.55]) was 69.51% (8.41). Accuracy was significantly above chance in 34 participants. This machine learning‐based approach provides a proof of principle for individualizing EEG targets for potential interventions.