Supplementary Materials for "DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding"

Key Points

• To present supplementary information supporting the development of DBConformer, a new method for EEG decoding.
• Introduced dual-branch convolutional transformer architecture for processing EEG signals.
• Utilized machine learning techniques for improved signal classification and decoding accuracy.
• Demonstrated the efficacy of the DBConformer model in EEG signal decoding.
• Provided supplementary data reinforcing experimental outcomes and algorithm performance.