Model comparison for automatic characterization and classification of average ERPs using visual oddball paradigm

Objective: To determine whether automated classifiers can be used for correctly identifying target categorization responses from averaged event-related potentials (ERPs) along with identifying appropriate features and classification models for computer-assisted investigation of attentional processes. Methods: ERPs were recorded during a target categorization task. Automated classification of average target ERPs versus average non-target ERPs was performed by extracting different combinations of features from the P300 and N200 components, which were used to train six classifiers: Euclidean classifier (EC), Mahalanobis discriminant (MD), quadratic classifier (QC), Fisher linear discriminant (FLD), multi-layer perceptron neural network (MLP) and support vector machine (SVM). Results: The best classification performance (accuracy: 91-92%; sensitivity: 85-86%; specificity: 95-99%) was provided by QC, MLP, SVM on feature vectors extracted from P300 recorded at multiple sites. In general, non-linear and non-parametric classifiers (QC, MLP, SVM) performed better than linear classifiers (EC, MD, FLD). The N200 did not explain variance beyond that of P300 recorded at multiple sites. Conclusions: The results suggest that automatic characterization and classification of average target and non-target ERPs is feasible. Features of P300 recorded at multiple sites used to train non-linear classifiers are recommended for optimal classification performance. Significance: Automatic characterization of target ERPs can provide an objective approach for detecting and diagnosing abnormalities and evaluating interventions for clinical populations, paving the way for future real-time monitoring of attentional processes.