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Bi-Stream Adaptation Network for Motor Imagery Decoding.

Zikai Wang, Ang Li, Zhenyu Wang

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|March 3, 2025

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This study introduces a novel Bi-Stream Adaptation Network (BSAN) to improve motor imagery (MI) classification by capturing multi-scale context and addressing EEG variations across sessions. The BSAN enhances classification performance and robustness.

Area of Science:

Neuroscience
Machine Learning
Biomedical Engineering

Background:

Motor imagery (MI) classification relies on neural activity from distinct brain regions, but performance is often limited by non-stationarity and cross-session variability in EEG data.
Understanding hidden contextual information within neural signals is crucial for enhancing MI classification accuracy.
Existing methods struggle to effectively address the global and local distribution shifts in EEG data across different sessions.

Purpose of the Study:

To propose a novel Bi-Stream Adaptation Network (BSAN) for motor imagery (MI) classification.
To generate multi-scale context dependencies and bridge cross-session discrepancies in EEG data.
To enhance the performance and robustness of MI classification systems.

Main Methods:

Related Experiment Videos

A Bi-attention module was developed to capture multi-scale temporal dependencies and identify key brain regions involved in MI.
A Bi-discriminator was employed for domain adaptation, addressing both global and local variations in EEG data.
The proposed BSAN was validated using extensive experiments on two public MI datasets.

Main Results:

The BSAN demonstrated significant improvements in the performance and robustness of motor imagery (MI) classification.
The network effectively generated multi-scale context dependencies, leading to better feature representation.
The domain adaptation strategy successfully mitigated cross-session discrepancies in EEG data.
The proposed BSAN outperformed several existing state-of-the-art methods in MI classification tasks.

Conclusions:

The Bi-Stream Adaptation Network (BSAN) offers a promising approach for robust and accurate motor imagery (MI) classification.
The integration of multi-scale context generation and cross-session domain adaptation is effective in handling EEG non-stationarity.
This work contributes to advancing the field of brain-computer interfaces by improving MI classification capabilities.