# Fast and reliable hand motor imagery decoding based on beta burst rate modulations

Sotirios Papadopoulos, Ludovic Darmet, Maciej J Szul, Marco Congedo, James Bonaiuto, Jérémie Mattout

► To cite this version:

Sotirios Papadopoulos, Ludovic Darmet, Maciej J Szul, Marco Congedo, James Bonaiuto, et al. Fast and reliable hand motor imagery decoding based on beta burst rate modulations. Journées CORTICO 2024, May 2024, Nancy, France. hal-04550519

HAL Id: hal-04550519

https://hal.science/hal-04550519

Submitted on 17 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Fast and reliable hand motor imagery decoding based on beta burst rate modulations

Sotirios Papadopoulos, Ludovic Darmet, Maciej J Szul, Marco Congedo, James Bonaiuto, Jérémie Mattout

► To cite this version:

Sotirios Papadopoulos, Ludovic Darmet, Maciej J Szul, Marco Congedo, James Bonaiuto, et al. Fast and reliable hand motor imagery decoding based on beta burst rate modulations. CORTICO 2024 - sixième édition des Journées scientifiques CORTICO, May 2024, Nancy, France. hal-04550519

HAL Id: hal-04550519

https://hal.science/hal-04550519v1

Submitted on 17 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Fast and reliable hand motor imagery decoding based on beta burst rate modulations

S. Papadopoulos<sup>1,2,3,*</sup>, L. Darmet<sup>1,2,3</sup>, M. J. Szul<sup>1,3</sup>, M. Congedo<sup>4</sup>, J. J. Bonaiuto<sup>1,3,†</sup>, J. Mattout<sup>1,2,†</sup>

<sup>1</sup> University Lyon 1, Lyon, France; <sup>2</sup> Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France; <sup>3</sup> Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Lyon, France; <sup>4</sup> GIPSA-lab, University Grenoble Alpes, CNRS, Grenoble-INP, Grenoble, France

* e-mail: sotirios.papadopoulos@univ-lyon1.fr, † these authors contributed equally

Our understanding of motor-related processes has been significantly influenced by the description of the event-related desynchronization and synchronization phenomena in the mu (~8-13 Hz) and beta (~13-30 Hz) frequency bands [1,2,3]. Accordingly, non-invasive brain-computer interface (BCI) applications exploiting motor imagery (MI) usually depend on spatially- and band-limited power changes as the brain markers of interest. Yet, numerous studies have questioned the role of signal power as the best descriptor of movement-related brain activity modulations. On a single-trial level, beta band activity is characterized by short, transient, and heterogeneous events termed bursts rather than sustained oscillations [4,5,6].

In a recent study we analyzed the activity of channels C3 and C4 from multiple, open EEG datasets [7] with participants performing MI tasks. We demonstrated that a beta burst analysis of hand MI binary classification tasks (i.e. "left" vs "right") is often superior to beta power in terms of classification score [8].

Here we expand upon this approach. We introduce an algorithm that constructs convolution kernels based on beta burst waveforms and creates classification features that are comparable to those of state-of-the-art methods. By independently transforming the EEG recordings with each kernel we compute a proxy of the waveform-resolved burst rate that is then spatially filtered using the common spatial pattern algorithm (CSP) [9]. Then, all spatial features are combined and used for classification. We adopt a time-resolved decoding strategy and show that, when compared to CSP-based signal power in the beta and mu bands, the waveform-resolved burst rate often yields superior decoding results only needing a short amount of data points, likely because it captures slow (mu-like) signal changes.

This analysis shows that BCI applications could benefit from utilizing such features and paves the way for a real-time implementation of the proposed methodology.

## References

[1] Pfurtscheller G 1981 Central beta rhythm during sensorimotor activities in man Electroencephalogr. Clin. Neurophysiol. 51 253–64

[2] Pfurtscheller G and Berghold A 1989 Patterns of cortical activation during planning of voluntary movement Electroencephalogr. Clin. Neurophysiol. 72 250–8

[3] Pfurtscheller G and Da Silva F L 1999 Event related EEG/MEG synchronization and desynchronization: basic principles Clin. Neurophysiol. 110 1842–57

[4] Jones S R 2016 When brain rhythms aren't 'rhythmic': implication for their mechanisms and meaning Curr. Opin. Neurobiol. 40 72–80

[5] S. Little, et al., Human motor cortical beta bursts relate to movement planning and response errors. *PloS Biol.* **17**, 1–30 (2019).

[6] M. J. Szul, et al., 2023 Diverse beta burst waveform motifs characterize movement-related cortical dynamics *Prog. Neurobiol.*165187.

[7] Jayaram V and Barachant A 2018 MOABB: Trustworthy algorithm benchmarking for BCIs *J. Neural Eng.* **15**.

[8] S. Papadopoulos, et al., Beta bursts question the ruling power for brain-computer interfaces, *J. Neural Eng.*, 2024, 21 (1), pp.016010.

[9] Blankertz B, et al., 2008 Invariant common spatial patterns: Alleviating nonstationarities in Brain-Computer Interfacing *Adv. Neural Inf. Process. Syst.* 20 - Proc. 2007 Conf. 1–8.
Fast and reliable hand motor imagery decoding based on beta burst rate modulations

S. Papadopoulos<sup>1,2,3,*</sup>, L. Darmet<sup>1,2,3</sup>, M. J. Szul<sup>1,3</sup>, M. Congedo<sup>4</sup>, J. J. Bonaiuto<sup>1,3,†</sup>, J. Mattout<sup>1,2,†</sup>

<sup>1</sup> University Lyon 1, Lyon, France; <sup>2</sup> Lyon Neuroscience Research Center, CRNL, INSERM, U1028, CNRS, UMR 5292, Lyon, France; <sup>3</sup> Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Lyon, France; <sup>4</sup> GIPSA-lab, University Grenoble Alpes, CNRS, Grenoble-INP, Grenoble, France

* e-mail: sotirios.papadopoulos@univ-lyon1.fr, † these authors contributed equally

Our understanding of motor-related processes has been significantly influenced by the description of the event-related desynchronization and synchronization phenomena in the mu (~8-13 Hz) and beta (~13-30 Hz) frequency bands [1,2,3]. Accordingly, non-invasive brain-computer interface (BCI) applications exploiting motor imagery (MI) usually depend on spatially- and band-limited power changes as the brain markers of interest. Yet, numerous studies have questioned the role of signal power as the best descriptor of movement-related brain activity modulations. On a single-trial level, beta band activity is characterized by short, transient, and heterogeneous events termed bursts rather than sustained oscillations [4,5,6].

In a recent study we analyzed the activity of channels C3 and C4 from multiple, open EEG datasets [7] with participants performing MI tasks. We demonstrated that a beta burst analysis of hand MI binary classification tasks (i.e. "left" vs "right") is often superior to beta power in terms of classification score [8].

Here we expand upon this approach. We introduce an algorithm that constructs convolution kernels based on beta burst waveforms and creates classification features that are comparable to those of state-of-the-art methods. By independently transforming the EEG recordings with each kernel we compute a proxy of the waveform-resolved burst rate that is then spatially filtered using the common spatial pattern algorithm (CSP) [9]. Then, all spatial features are combined and used for classification. We adopt a time-resolved decoding strategy and show that, when compared to CSP-based signal power in the beta and mu bands, the waveform-resolved burst rate often yields superior decoding results only needing a short amount of data points, likely because it captures slow (mu-like) signal changes.

This analysis shows that BCI applications could benefit from utilizing such features and paves the way for a real-time implementation of the proposed methodology.

## References

[1] Pfurtscheller G 1981 Central beta rhythm during sensorimotor activities in man Electroencephalogr. Clin. Neurophysiol. 51 253–64

[2] Pfurtscheller G and Berghold A 1989 Patterns of cortical activation during planning of voluntary movement Electroencephalogr. Clin. Neurophysiol. 72 250–8

[3] Pfurtscheller G and Da Silva F L 1999 Event related EEG/MEG synchronization and desynchronization: basic principles Clin. Neurophysiol. 110 1842–57

[4] Jones S R 2016 When brain rhythms aren't 'rhythmic': implication for their mechanisms and meaning Curr. Opin. Neurobiol. 40 72–80

[5] S. Little, et al., Human motor cortical beta bursts relate to movement planning and response errors. *PloS Biol.* **17**, 1–30 (2019).

[6] M. J. Szul, et al., 2023 Diverse beta burst waveform motifs characterize movement-related cortical dynamics *Prog. Neurobiol.*165187.

[7] Jayaram V and Barachant A 2018 MOABB: Trustworthy algorithm benchmarking for BCIs *J. Neural Eng.* **15**.

[8] S. Papadopoulos, et al., Beta bursts question the ruling power for brain-computer interfaces, *J. Neural Eng.*, 2024, 21 (1), pp.016010.

[9] Blankertz B, et al., 2008 Invariant common spatial patterns: Alleviating nonstationarities in Brain-Computer Interfacing *Adv. Neural Inf. Process. Syst.* 20 - Proc. 2007 Conf. 1–8.
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Time-resolved pseudo-online decoding results in “left” versus “right” hand motor imagery classification. Three feature creation techniques are assessed: convolution of raw signals with a selection of burst waveforms (red), beta band (yellow) and mu-beta (purple) band filtering. Each row corresponds to across-subjects average decoding accuracies of a different EEG dataset. The first two columns correspond to a paradigm based on the incremental window approach. Using steps of 100ms duration, a classifier is trained on all available data up to each time point. The last two columns correspond to a paradigm based on the sliding window approach. Using overlapping 1s-long windows (50ms step) a classifier is trained on all data of each window. For either approach, both a single filter and a filter bank are compared to the convolution technique. Above each subplot, colored lines depict permutation test results that indicate the feature extraction technique that yields superior decoding results.</figcaption></figure>
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Time-resolved pseudo-online decoding results in “left” versus “right” hand motor imagery classification. Three feature creation techniques are assessed: convolution of raw signals with a selection of burst waveforms (red), beta band (yellow) and mu-beta (purple) band filtering. Each row corresponds to across-subjects average decoding accuracies of a different EEG dataset. The first two columns correspond to a paradigm based on the incremental window approach. Using steps of 100ms duration, a classifier is trained on all available data up to each time point. The last two columns correspond to a paradigm based on the sliding window approach. Using overlapping 1s-long windows (50ms step) a classifier is trained on all data of each window. For either approach, both a single filter and a filter bank are compared to the convolution technique. Above each subplot, colored lines depict permutation test results that indicate the feature extraction technique that yields superior decoding results.</figcaption></figure>