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

Ziwei Wang; Hongbin Wang; Tianwang Jia; Xingyi He; S Li; Dongrui Wu

doi:10.5281/zenodo.20607441

October 25, 2025Open Access

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

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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.

Implication

Supplementary materials detail a novel EEG decoding method using a dual-branch convolutional transformer, suggesting advancements in neural signal analysis.

Abstract

Supplementary Materials for the IEEE JBHI paper “DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding”.

Discussion

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Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/6a2a526080c8f91e7f39e501 https://doi.org/10.5281/zenodo.20607441

View Full Paper

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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