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

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