Cortical Classification with Rhythm Entropy for Error Processing in Cocktail Party Environment Based on Scalp EEG Recording

Abstract

Using single-trial cortical signals calculated by weighted minimum norm solution estimation (WMNE), the present study explored a feature extraction method based on rhythm entropy to classify the scalp electroencephalography (EEG) signals of error response from that of correct response during performing auditory-track tasks in cocktail party environment. The classification rate achieved 89.7% with single-trial (≈700 ms) when using support vector machine(SVM) with the leave-one-out-cross-validation (LOOCV). And high discriminative regions mainly distributed at the medial frontal cortex (MFC), the left supplementary motor area (lSMA) and the right supplementary motor area (rSMA). The mean entropy value for error trials was significantly lower than that for correct trials in the discriminative cortices. By time-varying network analysis, different information flows changed among these discriminative regions with time, i.e. error processing showed a left-bias information flow, and correct processing presented a right-bias information flow. These findings revealed that the rhythm information based on single cortical signals could be well used to describe characteristics of error-related EEG signals and further provided a novel application about auditory attention for brain computer interfaces (BCIs).