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Source-free domain adaptation for SSVEP-based brain-computer interfaces
Osman Berke Guney1, Deniz Kucukahmetler2, Huseyin Ozkan3
1Department of Electrical and Computer Engineering, Boston University, Boston, MA, United States of America.
Journal of Neural Engineering
|September 26, 2025
Summary
This study introduces a new brain-computer interface (BCI) method that adapts deep neural networks for users with speech difficulties. It removes the need for calibration, improving user comfort and communication speed.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Machine Learning
Background:
- Brain-computer interfaces (BCI) using steady-state visually evoked potentials (SSVEP) aid individuals with speech impairments.
- Current SSVEP-BCI spellers often require lengthy calibration, causing user discomfort and hindering adoption.
Purpose of the Study:
- To develop a novel deep neural network (DNN) adaptation method for SSVEP-BCI spellers that eliminates the need for user-specific calibration.
- To enhance user comfort and accelerate the adoption of BCI technology.
Main Methods:
- Proposed a self-supervised deep learning approach to adapt a pre-trained DNN to a new user (target domain) using only unlabeled data.
- Introduced a custom loss function comprising self-adaptation (pseudo-labeling) and local-regularity terms to leverage data structure.
Main Results:
- Achieved high information transfer rates (ITRs) of 201.15 bits/min on the benchmark dataset and 145.02 bits/min on the BETA dataset.
- Demonstrated superior performance compared to existing state-of-the-art methods.
Conclusions:
- The proposed method significantly improves user comfort by removing the calibration requirement.
- This approach maintains high character identification accuracy and ITR, paving the way for wider BCI adoption in daily life.
