Diverse beta burst waveform motifs characterize movement-related cortical dynamics

Szul, Maciej; Papadopoulos, Sotirios; Alavizadeh, Sanaz; Daligaut, Sébastien; Schwartz, Denis; Mattout, Jérémie; Bonaiuto, James

doi:10.1016/j.pneurobio.2023.102490

Cited by 10 publications

(19 citation statements)

References 119 publications

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“…This finding corroborates previous reports of varying levels of discriminating information across different temporal windows of signals from separate tasks [27,28,53]. In fact, a recent analysis of motor imagery data suggested that event-related beta activity is composed of numerous transient bursts with different underlying waveforms [54].…”

Section: Elucidating Task-specific Eeg Dynamicssupporting

confidence: 91%

“…For example, they may not have consistently performed the task for the entire duration of the trial or employed inconsistent strategies between trials [50]. Alternatively, their task-related EEG may have comprised short transient somatosensory rhythm burst dynamics [54]. Future studies may query users about their mental task strategies and investigate whether task-specific EEG dynamics can be altered by changing those strategies.…”

Section: Elucidating Task-specific Eeg Dynamicsmentioning

confidence: 99%

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Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs

Ivanov,

Lio,

Chau

2023

J. Neural Eng.

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Objective. While electroencephalography (EEG)-based brain–computer interfaces (BCIs) have many potential clinical applications, their use is impeded by poor performance for many users. To improve BCI performance, either via enhanced signal processing or user training, it is critical to understand and describe each user’s ability to perform mental control tasks and produce discernible EEG patterns. While classification accuracy has predominantly been used to assess user performance, limitations and criticisms of this approach have emerged, thus prompting the need to develop novel user assessment approaches with greater descriptive capability. Here, we propose a combination of unsupervised clustering and Markov chain models to assess and describe user skill. Approach. Using unsupervised K-means clustering, we segmented the EEG signal space into regions representing pattern states that users could produce. A user’s movement through these pattern states while performing different tasks was modeled using Markov chains. Finally, using the steady-state distributions and entropy rates of the Markov chains, we proposed two metrics taskDistinct and relativeTaskInconsistency to assess, respectively, a user’s ability to (i) produce distinct task-specific patterns for each mental task and (ii) maintain consistent patterns during individual tasks. Main results. Analysis of data from 14 adolescents using a three-class BCI revealed significant correlations between the taskDistinct and relativeTaskInconsistency metrics and classification F1 score. Moreover, analysis of the pattern states and Markov chain models yielded descriptive information regarding user performance not immediately apparent from classification accuracy. Significance. Our proposed user assessment method can be used in concert with classifier-based analysis to further understand the extent to which users produce task-specific, time-evolving EEG patterns. In turn, this information could be used to enhance user training or classifier design.

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Section: Elucidating Task-specific Eeg Dynamicssupporting

confidence: 91%

Section: Elucidating Task-specific Eeg Dynamicsmentioning

confidence: 99%

Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs

Ivanov,

Lio,

Chau

2023

J. Neural Eng.

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“…In an attempt to understand why, we compared multiple representations of beta activity modulation during the MI task. We showed that bursts of different shapes are selectively modulated following task onset, with distinct waveforms occurring with different probability during different points in time [100] (figures 4 and 5). This modulation can be encoded either by TF-derived features, or alternatively, burst waveforms.…”

Section: Discussionmentioning

confidence: 99%

“…Using a previously published iterative, adaptive procedure, we identified bursts within the beta frequency range from the TF matrix, and then extracted their waveforms from the “raw” time series (after low pass filtering as pre-processing) within a fixed time window of 260 ms, centered on the burst peak [100]. Due to inability to parameterize spectra from all datasets we subtracted twice the standard deviation of the TF before fitting each peak as a 2D Gaussian, instead of subtracting the aperiodic activity from the TF matrices [81,101,102], before detecting beta bursts.…”

Section: Methodsmentioning

confidence: 99%

“…Previous work from our group has demonstrated that principal component analysis (PCA) [104] (scikit-learn package, v1.0.2) can be used to understand how the rates of bursts with different waveforms are modulated during reaching movements [100]. In order to construct features suitable for classification, we projected the burst dictionary along each principal component.…”

Section: Methodsmentioning

confidence: 99%

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Beta bursts question the ruling power for brain-computer interfaces

Papadopoulos,

Szul,

Congedo

et al. 2023

Preprint

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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-pass filtered power changes or covariance matrices which also describe co-varying power changes in signals recorded from different 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 bio-markers.

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Age-related differences in how the shape of alpha and beta oscillations change during reaction time tasks

Opie,

Hughes,

Puri

2024

Neurobiology of Aging

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Diverse beta burst waveform motifs characterize movement-related cortical dynamics

Cited by 10 publications

References 119 publications

Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs

Towards user-centric BCI design: Markov chain-based user assessment for mental imagery EEG-BCIs

Beta bursts question the ruling power for brain-computer interfaces

Age-related differences in how the shape of alpha and beta oscillations change during reaction time tasks

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