The Spatio-Temporal Equalization for Evoked or Event-Related Potential Detection in Multichannel EEG Data.

Abstract

Evoked or Event-Related Potential (EP/ERP) detection is a major area of interest within the domain of EEG (electroencephalography) signal processing. While traditional methods of EEG processing have mostly focused on enhancing signal components, few have explored background noise suppression techniques. Optimizing the suppression of background noise can play a critical role in improving the performance of EP/ERP detection. In this study, a spatio-temporal equalization (STE) method was proposed based on the Multivariate Autoregressive (MVAR) model, which has been shown to suppress the spatio-temporal correlation of background noise and improve the EEG signal detection performance. For practical applications, two optimization schemes based on the spatio-temporal equalization method were designed to solve two common challenges in EEG signal detection: P300 and steady state visual evoked potentials. Our results demonstrated that the STE method effectively improves recognition performance of evoked or event-related potential detection. Additionally, the STE method offers fewer parameters, lower computational complexity, and easier implementation. These attributes allow the STE approach to be extended as a preprocessing method which can be used in combination with existing techniques.