Non-Stationarity in Brain-Computer Interfaces: An Analytical Perspective

TL;DR

This paper reviews the causes and effects of EEG non-stationarity in Brain-Computer Interfaces, emphasizing the importance of detecting and correcting covariate shift to enhance system robustness and accuracy.

Contribution

It provides an analytical overview of non-stationarity in EEG signals and discusses recent methods for detecting and correcting covariate shift in BCI applications.

Findings

Non-stationarity causes significant challenges in BCI performance.

Detecting covariate shift is crucial for improving EEG signal reliability.

Signal processing techniques can mitigate the effects of non-stationarity.

Abstract

Non-invasive Brain-Computer Interface (BCI) systems based on electroencephalography (EEG) signals suffer from multiple obstacles to reach a wide adoption in clinical settings for communication or rehabilitation. Among these challenges, the non-stationarity of the EEG signal is a key problem as it leads to various changes in the signal. There are changes within a session, across sessions, and across individuals. Variations over time for a given individual must be carefully managed to improve the BCI performance, including its accuracy, reliability, and robustness over time. This review paper presents and discusses the causes of non-stationarity in the EEG signal, along with its consequences for BCI applications, including covariate shift. The paper reviews recent studies on covariate shift, focusing on methods for detecting and correcting this phenomenon. Signal processing and machine learning techniques can be employed to normalize the EEG signal and address the covariate shift.