Study of cortical characteristics in operators of BCI with tactile feedback

A Brain-Computer Interface (BCI) is a technology that enables the exchange of information between the brain and external devices by processing electrical signals from the cerebral hemispheres. This technology can be applied in the development of neurotrainers for individuals who have had a stroke. In this work, we examined the classification accuracy of motor imagery-based Brain-Computer Interfaces with tactile feedback and assessed corticospinal excitability. The study has shown that the EEG signal classification accuracy results were compared between two BCI variants: one without feedback and one with tactile feedback. The average results across all participants showed no statistically significant difference, with accuracy rates of 63.04% for BCI without feedback and 65.58% for BCI with tactile feedback. We found that when participants engaged in imagined movements, there was a statistically significant increase in MEP amplitude compared to a "rest" state, regardless of their prior training. This suggests an increase in corticospinal excitability during imagined movements.