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EESCAN: An EEG and Eye Movement Fusion Network Combining Intra-modal Self-attention and Inter-modal Bidirectional

Jiayi Chen, Yanfei Lin, Xiaorong Gao

IEEE Transactions on Bio-Medical Engineering

|July 15, 2026

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The EESCAN network enhances dual Rapid Serial Visual Presentation (RSVP) brain-computer interfaces (BCIs) by fusing electroencephalography (EEG) and electromagnetic (EM) signals. This improves classification accuracy for gaze-independent device control.

Area of Science:

Neuroscience
Biomedical Engineering
Computer Science

Background:

Rapid Serial Visual Presentation (RSVP) is used in human-computer interactions, but single-stream paradigms risk detection errors.
Dual RSVP enhances classification robustness by using multiple image streams to increase target detection.
Existing methods for dual RSVP face challenges in accurately classifying complex data streams.

Purpose of the Study:

To introduce the EEG-EM Self Attention and Cross Attention Network (EESCAN) for improved classification in dual RSVP paradigms.
To develop a robust system for gaze-independent device control using dual RSVP.
To enhance the fusion of electroencephalography (EEG) and electromagnetic (EM) signals for brain-computer interfaces (BCIs).

Main Methods:

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Published on: June 17, 2019

A symmetric two-stream backbone processes EEG and EM signals, incorporating convolution and self-attention modules for feature extraction.

An inter-modal bidirectional interaction module facilitates complementary information exchange between EEG and EM data.

A dynamic reweighting and fusion module adaptively adjusts feature contributions, integrated into a VR-based dual RSVP robotic arm control system.

Main Results:

The EESCAN network demonstrated superior performance compared to existing decoding methods and single-modal baselines in classifying dual RSVP data.
Analysis of EEG and EM data from 21 subjects confirmed the network's effectiveness.
Ablation studies and visualizations validated the contribution of each module within the EESCAN architecture.

Conclusions:

The EESCAN network effectively fuses EEG and EM signals for dual RSVP paradigms, utilizing intra-modal self-attention, inter-modal interaction, and dynamic fusion.
EESCAN significantly boosts classification performance in dual RSVP-based BCIs.
The developed gaze-independent control system offers a viable solution for individuals with limited gaze mobility.