Neural mechanisms of training in Brain-Computer Interface: ABiophysical modeling approach

Abstract

Brain-Computer Interface (BCI) is a system that translates neural activity into commands, allowing direct communication between the brain and external devices. Despite its clinical application, BCI systems are unable to robustly capture subjects intent due to a limited understanding of the neural mechanisms underlying BCI control. To address this issue, we introduce a biophysical linear neural mass model to investigate the associated neural mechanisms of motor imagery-based BCI experiments by modulating the scale-free spectral characteristics of afferent inputs. We tailor this motor imagery neural mass model (mi-NMM) to simulate both motor imagery task and resting state, and apply this approach to a cohort of 19 healthy subjects trained over four sessions where magnetoencephalography (MEG) and electroencephalography (EEG) signals were simultaneously recorded. Task-dependent modulation of scale-free cortical dynamics reveals training-induced excitatory-inhibitory reconfiguration in BCI training. The intra-regional neural connectivity strengths and time scales of the modeled excitatory and inhibitory neural mass populations capture changes in neural activity across conditions and sessions. Those changes appear in important areas of the sensorimotor cortex, relevant for motor imagery tasks. We observed these effects in both EEG and MEG modalities. These findings provide insights into the underlying neural mechanisms in a motor imagery task in BCI, paving the way to tailored BCI training protocols. Significance StatementBrain-computer interface (BCI) holds great promise in allowing direct communication between the brain and external devices, yet many users struggle to learn it reliably, largely because the neural mechanisms underlying BCI training remain poorly understood - thus lack of biologically grounded training protocols. This study introduces a biophysically grounded modeling framework that links large-scale brain dynamics to learning during motor imagery based BCI training. By explicitly modeling task-dependent cortical background input, we reveal how excitatory-inhibitory population dynamics reorganize with training, shifting from widespread cortical to targeted sensorimotor recruitment. Our results, replicated across EEG and MEG, identify excitatory population time scales as a robust neural marker of motor imagery learning. These findings provide mechanistic insight into BCI learning and offer principled targets for designing individualized BCI training protocols.