Biophysical modeling to inform performance in motor imagery-based Brain-Computer Interfaces - Réseau de recherche en Théorie des Systèmes Distribués, Modélisation, Analyse et Contrôle des Systèmes
Poster De Conférence Année : 2024

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

Résumé

Brain-Computer Interface (BCI), by translating brain activity into commands for communication or control, is a promising tool for patients who suffer from neuromuscular pathologies or lesions. Nevertheless, it fails to detect intents in 15-30 % of the BCI users, due notably to a poor understanding of the mechanisms underlying the BCI performance. Here, we aim at using a biophysically interpretable and analytical model to identify biophysical changes occurring while controlling a BCI. We hypothesized that excitatory and inhibitory neuronal populations model parameters will differ when comparing the performed tasks in a BCI setting. We used source-reconstructed magnetoencephalography signals in a BCI framework where 19 subjects were instructed to modulate their brain activity to control the position of a cursor displayed on a screen by either performing a motor imagery task or remaining at rest [1]. We divided the cohort into two subgroups, namely G1 and G2, with subjects who performed better or worse than the average respectively. We employed a linearized neural mass model to infer four biophysically realistic parameters from the estimation of the power spectra: two neural gains capturing overall synaptic strength between excitatory and inhibitory neuronal populations (g_ei) and among inhibitory neuronal populations (g_ii), time constant of the excitatory neuronal population (tau_e), and time constant of the inhibitory neuronal population (tau_i) [2]. We inferred the optimal model parameters to match the shape of the modeled power spectra with the empirical power spectra for each subject during both rest and MI. We then compared the model parameters between rest and MI. To check that the spectral power in the alpha frequency band carried relevant information, we performed statistical tests between the Rest and the MI conditions on data from G1 and G2. Whereas no significant difference between Rest and MI in G2, in G1 significant condition effects were observed in associative and sensorimotor regions (Fig 1A). Then, we studied to which extent the excitatory/inhibitory neuronal population parameters could differ depending on the performed task. The neural gain g_ei shows a significant condition effect in regions involved in visual motion processing in G1 and in regions involved in the default mode network in G2. The neural gain g_ii significantly differs between Rest and MI in regions involved in decision-making processes in G1 and in areas involved in attention processes in G2. The time constant tau_e shows no significant condition effect in G1 whereas in G2, such an effect was observed in areas involved in visual recognition. Lastly, the time constant tau_i shows a significant condition effect in regions involved in motor imagery performance and in decision making processes in G1 (Fig 1B) and in areas involved in attention processes in G2. These results indicate changes in the excitatory and the inhibitory between the conditions with an alteration of the inhibitory neuronal population activity over sensorimotor areas in the most responsive subjects only. These can be potentially used as biophysically realistic markers of BCI performance. [1] Corsi, M-C, et al. Functional disconnection of associative cortical areas predicts performance during BCI training. NeuroImage (2020), 209, 116500. [2] Raj A, et al. Spectral graph theory of brain oscillations. Human Brain Mapping. (2020), 41(11), 2980-2998.
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hal-04701021 , version 1 (18-09-2024)

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  • HAL Id : hal-04701021 , version 1

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Parul Verma, Marie-Constance Corsi. Biophysical modeling to inform performance in motor imagery-based Brain-Computer Interfaces. CNS 2024 - 33rd Annual Computational Neuroscience Meeting, Jul 2024, Natal, Brazil. ⟨hal-04701021⟩
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