# Biophysical modeling to inform performance in motor imagery-based Brain Computer Interfaces

Parul Verma, Marie-Constance Corsi

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## To cite this version:

Parul Verma, Marie-Constance Corsi. Biophysical modeling to inform performance in motor imagery-based Brain Computer Interfaces. BIOMAG 2024 - 23rd International Conference on Biomagnetism, Aug 2024, Sydney, Australia. hal-04701082

HAL Id: hal-04701082

https://inria.hal.science/hal-04701082v1

Submitted on 18 Sep 2024

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Parul Verma<fnref n="1" />, Marie-Constance Corsi²

## Brain-computer Interfaces (BCI) experiments

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The BCI task consisted of a standard 1D, two-target box task in which the subjects modulated their **a** and/or **β** activity. To hit the target-up, the subjects performed a sustained motor imagery (MI) of

their right-hand grasping and to hit the target-down, they remained at rest. Twenty subjects (aged 27.45 ± 4.01 years, 12 men), all right-handed participated in the study.

Magnetoencephalography (MEG) data were preprocessed using the Independent Component Analysis method, followed by the source reconstruction on the epoched data [1].

## Biophysical Model of Brain Oscillations

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## Power spectral density at rest and while performing motor imagery

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Power spectral density (PSD) of all the subjects and their average (in bold) in the left superior parietal gyrus as an example. Blue: rest; red: motor imagery (MI). A: MEG PSD. B: PSD from model fitting. We see a decrease in the beta band in MI with both MEG and model.

## Changes in β frequency band spectral power while controlling a BCI

Together with the left precentral, the left postcentral, and the bilateral precuneus are the main regions where the beta power differs the most between conditions (p < 0.05). These regions are involved respectively in motor imagery, mental imagery planning and memory.

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## Changes in biophysical model parameters while controlling a BCI

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Model was fit to frequency spectra from rest and MI task for every subject and every region. Model parameters were compared between rest and MI only for the regions where beta spectral power was different.

The neural gain $g_{ii}$ shows a condition effect in regions involved in mental imagery planning and mental imagery (resp. in precuneus and supramarginal gyrus [3]).

The time constant $\tau_e$ presents a condition effect in pars opercularis involved in decision making. The noise parameter $f$ shows a condition effect in areas involved in mental strategies and visual perception (resp. precuneus and middle temporal gyrus).

## Contact

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parulv@iitm.ac.in

marie-constance.corsi@inria.fr

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@parulv1

@MConstanceCorsi

## Conclusions

- An overall reduction in the spectral power in the beta band is observed with MEG while performing MI as compared to rest in regions located over (pre)motor areas, in line with previous studies [4]

- Both the neural gains and time constants differ in regions involved in motor imagery, motor planning, and decision making.

## Future directions

- Identify markers of BCI performance

- Track differences in model parameters with training progression

- Model motor imagery-based BCI training explicitly

## Acknowledgements

The authors acknowledge support from Transatlantic Research Partnership from the FACE Foundation.

## References

[1] Corsi, M-C, et al. Functional disconnection of associative cortical areas predicts performance during BCI training. NeuroImage (2020).

[2] Verma, P., et al. Spectral graph theory of brain oscillations -revisited and improved. NeuroImage (2022).

[3] Hétu et al. The neural network of motor imagery: an ALE meta-analysis, Neuroscience & Biobehavioral Reviews (2013).

[4] Corsi, M-C, Sorrentino, P et al, Measuring Neuronal Avalanches to inform Brain-Computer Interfaces. iScience (2024).

<footnote n="1">¹Indian Institute of Technology, Madras, India
²Sorbonne Université, Institut du Cerveau – Paris Brain Institute, ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France</footnote>