Classification of event-related potentials associated with response errors in actors and observers based on autoregressive modeling

Abstract

Event-Related Potentials (ERPs) provide non-invasive measurements of the electrical activity on the scalp related to the processing of stimuli and preparation of responses by the brain. In this paper an ERP-signal classification method is proposed for discriminating between ERPs of correct and incorrect responses of actors and of observers seeing an actor making such responses. The classification method targeted signals containing error-related negativity (ERN) and error positivity (Pe) components, which are typically associated with error processing in the human brain. Feature extraction consisted of Multivariate Autoregressive modeling combined with the Simulated Annealing technique. The resulting information was subsequently classified by means of an Artificial Neural Network (ANN) using back-propagation algorithm under the "leave-one-out cross-validation" scenario and the Fuzzy C-Means (FCM) algorithm. The ANN consisted of a multi-layer perceptron (MLP). The approach yielded classification rates of up to 85%, both for the actors' correct and incorrect responses and the corresponding ERPs of the observers. The electrodes needed for such classifications were situated mainly at central and frontal areas. Results provide indications that the classification of the ERN is achievable. Furthermore, the availability of the Pe signals, in addition to the ERN, improves the classification, and this is more pronounced for observers' signals. The proposed ERP-signal classification method provides a promising tool to study error detection and observational-learning mechanisms in performance monitoring and joint-action research, in both healthy and patient populations.

Fig. (1)

Block diagram of the proposed system for the classification of ERP waveforms into two classes. In case the system was applied for classifying actions, Class 1 corresponded to correct actions while Class 2 to erroneous actions. In case the system was applied for classifying observations, Class 1 corresponded to observations of correct actions while Class 2 to observations of erroneous actions.

Fig. (2)

Montage used for the EEG recording. The electrodes on the central line correspond to those of the 10%-system. Electrodes used were 1 to 34 and 37 to 49. Sub-region SR-1 is included inside the thick dashed line and sub-region SR-2 inside the thick dotted line.

Fig. (3)

The grand average curves for electrodes 1,8,9,11,13-15,17,19: (a) for actors’ ERPs correct (dashed lines) and incorrect (solid lines) responses, and (b) for ERPs of observers’ observations of correct (dashed lines) and incorrect (solid lines) responses. The vertical axis has negative values upwards. The number above each pair of curves indicates the electrode.

Fig. (4)

Visual representation of the electrode combinations (indicated by squares) corresponding to the best classification rate achieved for actors’ actions and observers’ observations and for the two sub-regions SR-1 and SR-2 (regions within the thick dashed and dotted lines, respectively). Electrode combinations for the best classification rate achieved for actors’ actions: (a) in the time window -6 to 146 msec for SR-1, and (b) in the time window -6 to 500 msec for SR-2, using both the ANN classifier with cross validation. Electrode combinations for the best classification rate achieved for observers’ observations: (c) in the time window -6 to 700 msec for SR-1 using the ANN classifier with cross-validation, and (d) in the time window -6 to 500 msec for SR-2 using the FCM method.

Fig. (5)

Performance of the classification system for different model orders used by the MVAR/SA method and the MLP-ANN classifier for the sub-region SR-1. (a) using actors’ actions in the time window -6 to 146 msec and (b) using observers’ Observation of actions in the time window -6 to 700 msec.