High-Performance Cross-Subject Decoding of Multiclass Rhythmic Motor Imagery Using EEG Data from 100 Subjects

Yuxuan Wei; Ximing Mai; Yang Li; Ruijie Luo; Ruijia Cheng; Jianjun Meng

doi:10.1109/tnsre.2026.3676837

January 1, 2026IEEE Transactions on Neural Systems and Rehabilitation EngineeringOpen Access

High-Performance Cross-Subject Decoding of Multiclass Rhythmic Motor Imagery Using EEG Data from 100 Subjects

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Key Points

This research aims to improve cross-subject decoding accuracy of motor imagery using EEG data from multiple subjects.
Developed new decoding models for EEG data analysis
Utilized a large dataset from 100 subjects for enhanced model training
Analyzed data quality to optimize performance
Achieved high-performance decoding of motor imagery across subjects
Demonstrated significant improvements in classification accuracy
Provided open-source code for further research and applications

Implication

Novel insights reveal effective cross-subject decoding of motor imagery in EEG data, suggesting new model designs and data quality improvements.

Abstract

Overall, our study provides novel insights into cross-subject EEG decoding from the perspectives of model design, data scale and quality. The code is available in https://github.com/SJTUwyxuan/RhythmicMI-CrossSubject.

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Cite This Study

Wei et al. (2026) studied this question.

synapsesocial.com/papers/69c4cc02fdc3bde4489176b6 https://doi.org/10.1109/tnsre.2026.3676837

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High-Performance Cross-Subject Decoding of Multiclass Rhythmic Motor Imagery Using EEG Data from 100 Subjects

Key Points

Implication

Abstract

Discussion

Cite This Study

High-Performance Cross-Subject Decoding of Multiclass Rhythmic Motor Imagery Using EEG Data from 100 Subjects

Key Points

Implication

Abstract

Discussion

Cite This Study

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