# Research on Adaptive Discriminating Method of Brain–Computer Interface for Motor Imagination

## TL;DR

This study explores how brain networks during imagined movements can predict how well people adapt to brain-computer interfaces.

## Contribution

The study identifies tongue imagination as a potential predictor of motor imagery BCI adaptability using functional network analysis.

## Key findings

- Tongue imagination shows strong correlation with motor imagery BCI adaptability.
- Nodal degree and path length in the right hemisphere correlate with classification accuracy.
- Functional network features can predict individual BCI performance.

## Abstract

(1) Background: Brain–computer interface (BCI) technology represents a cutting-edge field that integrates brain intelligence with machine intelligence. Unlike BCIs that rely on external stimuli, motor imagery-based BCIs (MI-BCIs) generate usable brain signals based on an individual’s imagination of specific motor actions. Due to the highly individualized nature of these signals, identifying individuals who are better suited for MI-BCI applications and improving its efficiency is critical. (2) Methods: This study collected four motor imagery tasks (left hand, right hand, foot, and tongue) from 50 healthy subjects and evaluated MI-BCI adaptability through classification accuracy. Functional networks were constructed using the weighted phase lag index (WPLI), and relevant graph theory parameters were calculated to explore the relationship between motor imagery adaptability and functional networks. (3) Results: Research has demonstrated a strong correlation between the network characteristics of tongue imagination and MI-BCI adaptability. Specifically, the nodal degree and characteristic path length in the right hemisphere were found to be significantly correlated with classification accuracy (p < 0.05). (4) Conclusions: The findings of this study offer new insights into the functional network mechanisms of motor imagery, suggesting that tongue imagination holds potential as a predictor of MI-BCI adaptability.