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A probabilistic approach for calibration time reduction in hybrid EEG-fTCD brain-computer interfaces.
1Electrical and Computer Engineering Department, University of Pittsburgh, Pittsburgh, PA, USA. afk17@pitt.edu.
Biomedical Engineering Online
|April 18, 2020
Summary
This study introduces a transfer learning method to reduce calibration for brain-computer interfaces (BCIs). The approach significantly decreases calibration needs for motor imagery (MI) BCIs, enhancing usability for individuals with disabilities.
Area of Science:
- Neuroscience
- Computer Science
- Biomedical Engineering
Background:
- Brain-computer interfaces (BCIs) typically require extensive user calibration.
- This calibration burden is a significant challenge, particularly for patients with disabilities.
- Current BCIs necessitate multiple calibration sessions for effective use.
Purpose of the Study:
- To reduce calibration requirements for EEG-fTCD hybrid BCIs.
- To improve the efficiency of BCIs for users, especially those with disabilities.
- To leverage transfer learning for data augmentation in BCI systems.
Main Methods:
- A probabilistic transfer learning approach was developed.
- Similar datasets from previous users were identified to augment current user data.
- Support vector machines and Bhattacharyya distance were used for feature extraction and similarity assessment.
Main Results:
- The transfer learning approach significantly improved BCI performance compared to no transfer learning.
- Calibration requirements were reduced by over 60% for the motor imagery (MI) paradigm.
- A reduction of up to 17.31% in calibration was observed for the mental rotation/word generation (MR/WG) paradigms.
Conclusions:
- Motor imagery (MI) data demonstrated superior generalization across subjects.
- The proposed method effectively reduces calibration needs, making BCIs more accessible.
- Transfer learning is a promising technique for optimizing BCI system calibration.
