# Exploring distinct beta band activity representations during hand motor imagery

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

► To cite this version:

Sotirios Papadopoulos, Maciej J Szul, Marco Congedo, James Bonaiuto, Jérémie Mattout. Exploring distinct beta band activity representations during hand motor imagery. CORTICO 2023 - Journées CORTICO 2023, May 2023, Paris, France. hal-04550508

HAL Id: hal-04550508

https://hal.science/hal-04550508

Submitted on 17 Apr 2024

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# Exploring distinct beta band activity representations during hand motor imagery

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

► To cite this version:

Sotirios Papadopoulos, Maciej J Szul, Marco Congedo, James Bonaiuto, Jérémie Mattout. Exploring distinct beta band activity representations during hand motor imagery. CORTICO 2023 - cinquième édition des Journées scientifiques CORTICO, May 2023, Paris, France. hal-04550508

HAL Id: hal-04550508

https://hal.science/hal-04550508v1

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
Exploring distinct beta band activity representations during hand motor imagery

Sotirios Papadopoulos¹,²,³,*, M. J. Szul¹,³, M. Congedo⁴, J. J. Bonaiuto¹,³,†, J. Mattout¹,²,†

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

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

Brain-computer interfaces (BCI) for motor-related tasks have traditionally relied on band-limited power changes as the classification feature of interest. This approach is based on seminal papers that described the first reliable non-invasively recorded neural markers for such tasks: event-related desynchronization (ERD) and synchronization (ERS) in the mu and beta frequency bands [1]. Recently, however, mounting evidence has suggested that beta band activity is neither sinusoidal nor sustained oscillations, but rather occurs as bursts [2]. These findings imply that the ERD/S patterns emerge only when studying trial-averaged activity and that signal power may thus not be the most appropriate neurophysiological marker to use in paradigms focusing on single-trial activity, like BCI. Instead, an alternative approach that describes beta burst rate modulations instead of beta power could be more sensitive for classification, as it may better reflect subtle, condition-specific changes.

To explore this hypothesis, we analyzed seven open EEG datasets contrasting “left” versus “right” hand motor imagery and available via the MOABB package [3]. We focused our analysis on channels C3 and C4 for each subject, and compared standard single trial classification approaches with a new burst detection and waveform analysis algorithm [4].

We show that classification features describing the rate modulation of bursts with distinct waveforms can be more informative than beta band power, and more reliable than conventional time-frequency based burst features such as overall burst rate, burst peak amplitude, and the temporal and frequency spans of bursts (Fig. 1). These results suggest the potential improvement in BCI classification accuracy and robustness one could achieve by considering such finer neurophysiological features. They also call for further exploration of the complex relationship between burst activity and signal power within the beta range.
Exploring distinct beta band activity representations during hand motor imagery

Sotirios Papadopoulos¹,²,³,*, M. J. Szul¹,³, M. Congedo⁴, J. J. Bonaiuto¹,³,†, J. Mattout¹,²,†

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

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

Brain-computer interfaces (BCI) for motor-related tasks have traditionally relied on band-limited power changes as the classification feature of interest. This approach is based on seminal papers that described the first reliable non-invasively recorded neural markers for such tasks: event-related desynchronization (ERD) and synchronization (ERS) in the mu and beta frequency bands [1]. Recently, however, mounting evidence has suggested that beta band activity is neither sinusoidal nor sustained oscillations, but rather occurs as bursts [2]. These findings imply that the ERD/S patterns emerge only when studying trial-averaged activity and that signal power may thus not be the most appropriate neurophysiological marker to use in paradigms focusing on single-trial activity, like BCI. Instead, an alternative approach that describes beta burst rate modulations instead of beta power could be more sensitive for classification, as it may better reflect subtle, condition-specific changes.

To explore this hypothesis, we analyzed seven open EEG datasets contrasting “left” versus “right” hand motor imagery and available via the MOABB package [3]. We focused our analysis on channels C3 and C4 for each subject, and compared standard single trial classification approaches with a new burst detection and waveform analysis algorithm [4].

We show that classification features describing the rate modulation of bursts with distinct waveforms can be more informative than beta band power, and more reliable than conventional time-frequency based burst features such as overall burst rate, burst peak amplitude, and the temporal and frequency spans of bursts (Fig. 1). These results suggest the potential improvement in BCI classification accuracy and robustness one could achieve by considering such finer neurophysiological features. They also call for further exploration of the complex relationship between burst activity and signal power within the beta range.
<figure><img src="image_2.png" /><figcaption>Figure 1. Average classification scores based on different feature extraction algorithms for seven open motor imagery datasets of “left vs right hand” tasks accessible via MOABB.</figcaption></figure>

References

[1] G. Pfurtscheller, C. Neuper, Motor imagery direct communication. *Proc. IEEE* **89**, 1123–1134 (2001).

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

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

[4] M. J. Szul, et al., Diverse beta burst waveform motifs characterize movement-related cortical dynamics. *BioRxiv* (2022).
<figure><img src="image_2.png" /><figcaption>Figure 1. Average classification scores based on different feature extraction algorithms for seven open motor imagery datasets of “left vs right hand” tasks accessible via MOABB.</figcaption></figure>

References

[1] G. Pfurtscheller, C. Neuper, Motor imagery direct communication. *Proc. IEEE* **89**, 1123–1134 (2001).

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

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

[4] M. J. Szul, et al., Diverse beta burst waveform motifs characterize movement-related cortical dynamics. *BioRxiv* (2022).