Biophysical modeling to inform performance in motor imagery-based Brain Computer Interfaces - INRIA 2
Poster De Conférence Année : 2024

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

Résumé

Brain-Computer Interface (BCI), translates brain activity into commands. Despite its clinical applications, it fails to detect intents in 30% of the users, due to a poor understanding of the associated mechanisms. Here, we use a mathematical model to identify biophysical changes occurring while controlling a BCI. We used a motor imagery-based BCI framework [1]. The cohort of 19 subjects was divided into G1 and G2 subgroups, with subjects who performed better or worse than the average. We inferred four biophysical parameters of a neural mass model from the estimation of the power spectra for each subject during both rest and MI with MEG: two neural gains capturing overall synaptic strength between excitatory and inhibitory neuronal population (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]. The model parameters show significant condition effects as follows: g_ei in regions involved in visual motion processing in G1 and in regions involved in the default mode network in G2; g_ii in regions involved in decision-making processes in G1 and in areas involved in attention processes in G2; tau_e in areas involved in visual recognition only in G2; tau_i in regions involved in motor imagery performance and in decision making processes in G1 and in areas involved in attention processes in G2. We observed changes in the excitatory/inhibitory neuronal activity over sensorimotor areas in the most responsive subjects only, eliciting potential markers of BCI performance. [1]M.-C. Corsi et al., “Functional disconnection of associative cortical areas predicts performance during BCI training,” NeuroImage, vol. 209, p. 116500, Apr. 2020, doi: 10.1016/j.neuroimage.2019.116500. [2]A. Raj et al., “Spectral graph theory of brain oscillations,” Hum. Brain Mapp., vol. 41, no. 11, pp. 2980–2998, Aug. 2020, doi: 10.1002/hbm.24991.
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hal-04701082 , version 1 (18-09-2024)

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  • HAL Id : hal-04701082 , version 1

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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⟩
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