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Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network
Qingguo Wei1, Chang Li2, Yijun Wang3
1Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction, Department of Electronic Engineering, School of Information Engineering, Nanchang University, Nanchang, 330031, China. wqg07@163.com.
Scientific Reports
|January 3, 2025
Summary
This study introduces eTRCA+sbCNN, a novel framework combining traditional and deep learning for Steady-State Visually Evoked Potential (SSVEP) brain-computer interfaces. This hybrid approach significantly enhances SSVEP signal recognition accuracy.
Area of Science:
- Neuroscience
- Computer Science
- Biomedical Engineering
Background:
- Steady-State Visually Evoked Potential (SSVEP) signals are crucial for brain-computer interfaces (BCIs).
- Traditional machine learning and deep learning networks offer distinct advantages for SSVEP decoding.
- An efficient integration strategy for these methods in SSVEP BCIs is currently lacking.
Purpose of the Study:
- To propose and validate an efficient framework for combining traditional and deep learning methods for SSVEP signal recognition.
- To enhance the performance of SSVEP-based BCIs by leveraging the complementary strengths of different algorithms.
Main Methods:
- A novel classification framework, eTRCA+sbCNN, is proposed, integrating ensemble task-related component analysis (eTRCA) and a sub-band convolutional neural network (sbCNN).
- Both eTRCA and sbCNN models are trained independently.
- Classification score vectors from both models are summed, and the SSVEP frequency with the highest summed score is selected.
Main Results:
- The eTRCA+sbCNN framework significantly improves classification performance compared to individual eTRCA and sbCNN models.
- Validation on two SSVEP BCI datasets demonstrates superior performance against state-of-the-art methods.
- The proposed method effectively utilizes the complementarity of feature signals from both approaches.
Conclusions:
- The eTRCA+sbCNN framework offers an effective strategy for integrating traditional and deep learning methods in SSVEP BCIs.
- This approach significantly enhances SSVEP classification accuracy, promoting the practical application of SSVEP-BCI systems.
