What question did this study set out to answer?

To develop a framework for categorizing motor imagery-based brain-computer interface (MI-BCI) users based on their brain dynamics.

February 11, 2026Open Access

A Neurophysiological Stratification Framework for Intermediate Motor Imagery-BCI Users Based on Independent Event-Related Brain Dynamics

XDXu DuanXi'an International Studies University SXSongyun Xie YCYujie CuiJilin University

Key Points

To develop a framework for categorizing motor imagery-based brain-computer interface (MI-BCI) users based on their brain dynamics.
Analyzed EEG data using Independent Component Analysis (ICA) to extract event-related desynchronization/synchronization components.
Integrated dipole localization with frequency-band power features for EEG interpretation.
Applied power spectral parameterization to refine power estimation of brain activities.
Conducted clustering analysis to classify participants into distinct performance groups.
Identified four user groups: good performers (24.8%), poor performers (35.8%), LgoodRpoor (27.5%), and LpoorRgood (11.9%).
LgoodRpoor performed well in left-hand motor imagery but poorly in right-hand motor imagery, and vice versa for LpoorRgood.
Significant differences in ipsilateral event-related synchronization (ERS) but not in contralateral event-related desynchronization (ERD).

Abstract

Background: Motor imagery-based brain-computer interfaces (MI-BCIs) enable individuals who are unable to perform physical movements to interact with the external world by imagining movements. Users are typically classified as good performers or BCI-illiterate based on the classification accuracy of distinct EEG patterns (e.g., 60% or 70%). Yet, studies show that approximately 70% of users fall within intermediate accuracies between 60% and 80%, and although exceed the chance level, they often fail to achieve reliable MI-BCI control. Intermediate users often exhibit asymmetric motor imagery abilities between left and right hands, highlighting the need for refined early assessment and stratified training approaches. Methods: We employed ICA to decompose each participant’s EEG data and extract independent ERD/ERS components as indicators using a rule-based automated framework. This framework integrated dipole localization, ERD/ERS characteristics, and frequency-band power features of ICs. Importantly, we applied a power spectral parameterization approach to remove the 1/f-like background activity in power estimation and used statistical methods to precisely estimate the latency and duration of ERD. The extracted indicators were subsequently subjected to clustering analysis to categorize participants into four groups. Results: In addition to good performers (24.8%) and poor performers (35.8%), two groups were identified: LgoodRpoor (27.5%), who performed well in left-hand MI but poorly in right-hand MI, and LpoorRgood (11.9%), who showed the opposite pattern. Notably, these unilateral performers did not show significant differences in contralateral ERD but exhibited substantial differences in ipsilateral ERS. Conclusions: The proposed independent event-related brain dynamics model enables more refined stratification of MI-BCI users. Findings from this characterization study may inform the design of graded training protocols, especially for users demonstrating unilateral motor imagery proficiency.

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