Improving Classification Accuracy of Detecting Error-Related Potentials using Two-stage Trained Neural Network Classifier

Error-Related Potential (Errp) is the bioelectric potential elicited in human brain as a result of the cognitive state of awareness when an error is perceived. Identifying ErrP from a single trial electroencephalogram (EEG) data can be used in taking corrective actions to fix the error or as a learning strategy in Brain Computer Interface (BCI) systems. The ErrP dataset recorded using EEG will contain both erroneous and correct actions. A classifier such as the Artificial Neural Network (ANN) can be trained to identify the erroneous versus correct action from a single trial EEG data. However, the classifier will have large number of parameters to be learned, and typically, the ErrP dataset is unbalanced with smaller number of erroneous trials. Therefore, the trained classifier may not generalize the data well. To classify the ErrP with better accuracy, an ANN architecture is proposed in this work. Learning the parameters of the ANN is carried out in two stages (Stage-1 and Stage-2) in the proposed method. The first stage of learning will have relatively large feature samples collected from several subjects. The first stage learning is aimed to capture the global characteristics of the ErrP. In the second stage, the pre-trained ANN classifier from the first stage is tuned for each subject. The ErrP dataset has two sessions dataset recorded from six subjects and the Stage-1 and Stage-2 training models are cross-validated. The overall classification accuracy achieved after cross-validation is 74.78 ± 3.43% and 86.03 ± 1.02% for erroneous and correct trials respectively. The improvement in the classification accuracy achieved is 12.67% and 15.51% for erroneous and correct trials respectively compared with the existing statistical classifier method. The method proposed shows an efficient way to train ANN classifier to achieve higher classification accuracy for unbalanced and smaller dataset such as ErrP.