An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces

Papadopoulos, Sotirios; Bonaiuto, James; Mattout, Jérémie

doi:10.3389/fnins.2021.824759

Cited by 6 publications

(3 citation statements)

References 109 publications

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“…The vast majority of non-invasive brain-computer interfaces (BCIs) try to identify and characterize single imagined movements using temporally averaged power in the mu and beta bands (Brodu et al, 2011; Herman et al, 2008; Pfurtscheller & Neuper, 2001). Most recent advances in the field rely on sophisticated machine learning techniques (Barachant et al, 2012; Llera et al, 2014; Lotte et al, 2018; Song et al, 2013), but we have recently argued that what is needed for further predictive power is a more fine-grained approach to feature extraction aimed at the temporal signatures of bursts of mu and beta activity (Papadopoulos et al, 2022). We have here demonstrated that averaged beta power includes many burst motifs that do not change in rate pre-movement.…”

Section: Discussionmentioning

confidence: 99%

Diverse beta burst waveform motifs characterize movement-related cortical dynamics

Szul

Papadopoulos

Alavizadeh

et al. 2022

Preprint

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Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.

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Section: Discussionmentioning

confidence: 99%

Diverse beta burst waveform motifs characterize movement-related cortical dynamics

Szul

Papadopoulos

Alavizadeh

et al. 2022

Preprint

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“…However, there has recently been a considerable paradigm shift towards considering transient signal features on the single trial level (Chen et al, 2021;Coleman et al, 2024;Jones, 2016;Little et al, 2019;Lundqvist et al, 2024Lundqvist et al, , 2016Rayson et al, 2023;Shin et al, 2017;Torrecillos et al, 2018;Vigué-Guix and Soto-Faraco, 2022;Wessel, 2020). Therefore, considering that computational models describing the neuronal generators of specific burst waveform shapes (Bonaiuto et al, 2021;Sherman et al, 2016;Szul et al, 2023) offer an improved theoretical interpretability of the observed signal modulations, applications leveraging such signal characteristics, like beta bursts, could potentially benefit from incorporating recent neuroscience findings (Papadopoulos et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Surfing beta burst waveforms to improve motor imagery-based BCI

Papadopoulos,

Darmet,

Szul

et al. 2024

Preprint

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Our 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.

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“…We have previously argued that analyzing beta burst activity should enable us to gain access to classification features that are at least as sensitive as beta band power [88]. If this hypothesis is valid, then we should be able to test it and verify it using publicly available datasets.…”

Section: Introductionmentioning

confidence: 99%

Beta bursts question the ruling power for brain–computer interfaces

Papadopoulos,

Szul,

Congedo

et al. 2024

J. Neural Eng.

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Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience. In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in “left vs. right hand” motor imagery tasks. Current decoding approaches for such tasks take advantage of the fact that motor imagery generates time-locked changes in induced power in the sensorimotor cortex, and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during motor imagery, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements. We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these biomarkers.

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An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces

Cited by 6 publications

References 109 publications

Diverse beta burst waveform motifs characterize movement-related cortical dynamics

Diverse beta burst waveform motifs characterize movement-related cortical dynamics

Surfing beta burst waveforms to improve motor imagery-based BCI

Beta bursts question the ruling power for brain–computer interfaces

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