Poster No:
1325
Submission Type:
Abstract Submission
Authors:
Apurba Debnath1, Marie-Constance Corsi2, Parul Verma1
Institutions:
1IIT Madras, Madras, India, 2Inria Paris, Paris, France
First Author:
Co-Author(s):
Introduction:
Brain-computer interface (BCI), is a system that translates neural activity into commands and allows direct communication between the brain and external devices. In spite of having many therapeutic applications, it is still unable to capture nearly 30% of the users' intents, due to a poor understanding of the associated mechanisms.
Methods:
To elucidate the underlying neural mechanisms, we utilized a mathematical model– Spectral Graph Model (SGM) (Raj et al., 2020), a hierarchical linear neural mass model, and a motor-imagery (MI) based BCI framework (Corsi et al., 2020) where a cohort of 19 subjects were trained across four sessions. Four biophysical parameters of the SGM were deduced from the estimation of each subject's power spectra using EEG during resting-state and MI: 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 the inhibitory neuronal population (tau_i). To investigate a potential condition effect, we applied the Wilcoxon test in MI vs Rest for every session separately and then did FDR correction for each parameter. Additionally, to determine whether the task-related SGM parameters change significantly over time, we computed the relative difference between MI and Rest for each parameter, and then performed the non-parametric Friedman test.
Results:
The SGM model parameters that show significant condition effects (P_FDR<0.1) session after session in case of MI vs Rest across all performers are– In session-1, g_ii shows significant effect for the regions involved in visual processing, motor and somatosensory functions; In session-2, tau_e shows significant effect for the regions involved in memory and visual processing functions; In session-3, tau_e shows significant effect in the regions involved in visual detection of patterns, visual memory recognition and visual mental imagery; In session-4, g_ei and g_ii both show significant effect in the regions involved in motor functions, cognitive processes linked notably to working memory, and decision-making, and visuo-spatial attention while tau_e shows significant effect in the regions for memory, and motor processing.
Moreover, we can also see the relative change in parameters across sessions due to MI compared to Rest((MI-Rest)/Rest) and observed that in the areas involved in motor, somatosensory, motor memory, learning complex motor skills, visual mental imagery, working memory, memory recall, & retrieval functions, the parameters g_ei, tau_e and tau_i present a session effect (pval<0.05).
Conclusions:
The results here provided an actionable insight into the underlying neural mechanisms of the BCI performance. We observed that the model parameters of the excitatory as well as inhibitory neural mass population were able to capture neural activity changes in the important sensorimotor cortex areas in various sessions, suggesting potential markers of the brain activation changes across sessions and states.
Higher Cognitive Functions:
Imagery
Learning and Memory:
Skill Learning
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Motor Behavior:
Brain Machine Interface 2
Novel Imaging Acquisition Methods:
EEG
Keywords:
Computational Neuroscience
Electroencephaolography (EEG)
ELECTROPHYSIOLOGY
Motor
Somatosensory
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
EEG/ERP
MEG
Structural MRI
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Free Surfer
Provide references using APA citation style.
Corsi, M.-C., Chavez, M., Schwartz, D., George, N., Hugueville, L., Kahn, A. E., Dupont, S., Bassett, D. S., & Fallani, F. D. V. (2020). Functional disconnection of associative cortical areas predicts performance during BCI training. NeuroImage, 209, 116500. https://doi.org/10.1016/j.neuroimage.2019.116500.
Raj, A., Cai, C., Xie, X., Palacios, E., Owen, J., Mukherjee, P., & Nagarajan, S. (2020). Spectral graph theory of brain oscillations. Human brain mapping, 41(11), 2980–2998. https://doi.org/10.1002/hbm.24991
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