Online detection of class-imbalanced error-related potentials evoked by motor imagery

Abstract

Abstract Objective. Error-related potentials (ErrPs) are spontaneous electroencephalogram signals related to the awareness of erroneous responses within brain domain. ErrPs-based correction mechanisms can be applied to motor imagery-brain–computer interface (MI-BCI) to prevent incorrect actions and ultimately improve the performance of the hybrid BCI. Many studies on ErrPs detection are mostly conducted under offline conditions with poor classification accuracy and the error rates of ErrPs are preset in advance, which is too ideal to apply in realistic applications. In order to solve these problems, a novel method based on adaptive autoregressive (AAR) model and common spatial pattern (CSP) is proposed for ErrPs feature extraction. In addition, an adaptive threshold classification method based spectral regression discriminant analysis (SRDA) is suggested for class-unbalanced ErrPs data to reduce the false positives and false negatives. Approach. As for ErrPs feature extraction, the AAR coefficients in the temporal domain and CSP in the spatial domain are fused. Given that the performance of different subjects’ MI tasks is different but stable, and the samples of ErrPs are class-imbalanced, an adaptive threshold based SRDA is suggested for classification. Two datasets are used in this paper. The open public clinical neuroprosthetics and brain interaction (CNBI) dataset is used to validate the performance of the proposed feature extraction algorithm and the real-time data recorded in our self-designed system is used to validate the performance of the proposed classification algorithm under class-imbalanced situations. Different from the pseudo-random paradigm, the ErrPs signals collected in our experiments are all elicited by four-class of online MI-BCI tasks, and the sample distribution is more natural and suitable for practical tests. Main results. The experimental results on the CNBI dataset show that the average accuracy and false positive rate for ErrPs detection are 94.1% and 8.1%, which outperforms methods using features extracted from a single domain. What’s more, although the ErrPs induction rate is affected by the performance of subjects’ MI-BCI tasks, experimental results on data recorded in the self-designed system prove that the ErrPs classification algorithm based on an adaptive threshold is robust under different ErrPs data distributions. Compared with two other methods, the proposed algorithm has advantages in all three measures which are accuracy, F1-score and false positive rate. Finally, ErrPs detection results were used to prevent wrong actions in a MI-BCI experiment, and it leads to a reduction of the hybrid BCI error rate from 48.9% to 24.3% in online tests. Significance. Both the AAR-CSP fused feature extraction and the adaptive threshold based SRDA classification methods suggested in our work are efficient in improving the ErrPs detection accuracy and reducing the false positives. In addition, by introducing ErrPs to multi-class MI-BCIs, the MI decoding results can be corrected after ErrPs are detected to avoid executing wrong instructions, thereby improving the BCI accuracy and lays the foundation for using MI-BCIs in practical applications.