Single-trial detection of EEG error-related potentials using modified power-law transformation

Abstract

Error processing in the human brain is characterized by a distinct neural response that is elicited when the outcome of an event is not the same as expected. This response is known as error-related potential (ErrP). Detection of error-related potentials from a single-trial data can lead the existing Brain-Computer Interface (BCI) systems one step closer to autonomous system. The non-stationarity of the Electroencephalograph (EEG) signal along with poor spatial resolution and low signal-to-noise ratio make the task of detecting ErrP on a single-trial basis challenging. The objective of this work is to propose a novel ErrP detection method based on temporal domain features of ErrP, particularly, the standard deviation measure. The difference in the standard deviation between the correct and the erroneous trials is verified using a statistical test and this information is subsequently used for ErrP detection. In the proposed method, the EEG electrodes from the frontocentral region of the brain are ranked using standard deviation based ranking criterion and a few top-ranked electrodes will be considered for single-trial detection of ErrP. Then, a modified power-law based transformation is proposed to enhance the discriminability between the correct and error trials. The discriminability is enhanced by transforming the samples lying outside a predefined amplitude range according to power-law equation and the samples within the selected amplitude range are transformed linearly. The modified power-law transformation has two parameters K and γ. The parameter K defines the pre-defined amplitude range and γ is the power in the standard power-law equation. The proposed ErrP detection method is tested on a publicly available EEG database of 10 subjects. The proposed transformation increases the discriminability between the erroneous and correct trials and achieves the best trade-off between the sensitivity and the specificity for detecting ErrP on single-trial basis compared to existing methods. The average sensitivity and specificity achieved is 86.17±9.05% and 92.05±3.69% respectively. The higher sensitivity of detection erroneous trials can be used to faithfully correct the wrongly decoded signal in the EEG based BCI system.