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A novel semi-supervised meta learning method for subject-transfer brain-computer interface.

Jingcong Li¹, Fei Wang¹, Haiyun Huang¹

¹School of Software, South China Normal University, Guangzhou, China; Pazhou Lab, Guangzhou, China.

Neural Networks : the Official Journal of the International Neural Network Society

|April 16, 2023

Summary

This summary is machine-generated.

This study introduces a semi-supervised meta learning (SSML) method to solve the brain-computer interface (BCI) subject calibration problem. SSML effectively transfers knowledge from existing subjects to new ones, improving BCI performance with limited labeled data.

Keywords:

Emotion recognition Event-related potential Meta learning Semi-supervised Sleep staging Transfer learning

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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

Area of Science:

Neuroscience
Machine Learning
Biomedical Engineering

Background:

Brain-computer interfaces (BCI) enable direct communication between the brain and external devices.
A significant challenge in BCI is the subject calibration problem, where models trained on one subject perform poorly on others.
Existing methods often require extensive labeled data for new subjects, which is impractical in many BCI applications.

Purpose of the Study:

To propose and evaluate a novel semi-supervised meta learning (SSML) method for effective subject-transfer calibration in BCI.
To address the scarcity or high cost of labeled data in BCI applications by leveraging readily available unlabeled data.
To demonstrate the applicability and efficiency of SSML across diverse BCI paradigms.

Main Methods:

Developed a model-agnostic meta learner trained on existing subjects.
Fine-tuned the meta learner using a semi-supervised approach with few labeled and many unlabeled samples from the target subject.
Evaluated the SSML method on three BCI paradigms: event-related potential detection, emotion recognition, and sleep staging.

Main Results:

Achieved high classification accuracies: 0.95 for event-related potential detection, 0.89 for emotion recognition, and 0.83 for sleep staging.
Demonstrated linear runtime complexity with respect to the number of target subject samples, enabling real-time BCI applications.
The SSML method showed significant effectiveness and potential for subject-transfer BCI calibration.

Conclusions:

Semi-supervised meta learning (SSML) offers a promising solution for the BCI subject calibration problem, especially when labeled data is scarce.
The proposed SSML method is efficient and adaptable to various BCI paradigms, paving the way for more practical BCI systems.
This study represents the first application of semi-supervised model-agnostic meta learning for subject calibration in BCI, validating its efficacy through rigorous experiments.