Surfing beta burst waveforms to improve motor imagery-based BCI

Abstract

AbstractOur understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during and following movement. The demonstration of reproducible, spatially-and band-limited signal power changes has, consequently, attracted the interest of non invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article we elaborate on this idea, proposing a signal processing algorithm that is comparable to-and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors thus being suitable for online applications. By adopting a time-resolved decoding approach we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches.Significance statementPatterns of waveform-specific burst rate comprise an alternative, neurophysiology-informed way of analyzing beta band activity during motor imagery (MI) tasks. By testing this method on multiple electroencephalography datasets and comparing its corresponding classification scores against those of conventional power-based features, this work demonstrates that brain-computer interface applications could benefit from utilizing beta burst activity. This activity gives access to a reliable decoding performance often requiring short recordings. As such, this study shows that waveform-specific beta burst rates encode information related to imagined (and presumably real) movements and serves as the first step for a real-time implementation of the proposed methodology.