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An online hybrid BCI combining SSVEP and EOG-based eye movements
Jun Zhang1, Shouwei Gao1, Kang Zhou1
1School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai, China.
Frontiers in Human Neuroscience
|March 6, 2023
Summary
This study introduces a hybrid brain-computer interface (hBCI) combining steady-state visual evoked potential (SSVEP) and eye movements. The novel system achieved high accuracy for improved brain-computer interface performance.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Human-Computer Interaction
Background:
- Brain-computer interfaces (BCIs) enable communication and control via neural signals.
- Hybrid BCIs (hBCIs) integrate multiple modalities to enhance performance.
- Existing hBCIs often face limitations in accuracy and speed.
Purpose of the Study:
- To develop and evaluate an online hybrid BCI system.
- To combine steady-state visual evoked potential (SSVEP) and eye movements for improved BCI performance.
- To introduce a novel decision-making strategy for hBCI systems.
Main Methods:
- Developed an hBCI integrating SSVEP and eye movements (detected via electrooculography - EOG).
- Utilized Canonical Correlation Analysis (CCA) and Filter Bank CCA (FBCCA) for SSVEP detection.
- Implemented a decision-making method based on SSVEP and EOG features.
Main Results:
- The hybrid BCI system demonstrated high performance in experiments with healthy students.
- Achieved an average accuracy of 94.75%.
- Reached an average information transfer rate of 108.63 bits/min.
Conclusions:
- The proposed online hBCI effectively combines SSVEP and eye movements.
- The novel decision-making approach enhances the overall performance of the hybrid BCI system.
- This approach offers a promising direction for developing more efficient and accurate BCI technologies.
