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Leveraging anatomical information to improve transfer learning in brain-computer interfaces.

Mark Wronkiewicz¹, Eric Larson, Adrian K C Lee

¹Graduate Program in Neuroscience University of Washington, Box 357270, Seattle, WA 98195, USA.

Journal of Neural Engineering

|July 15, 2015

Summary

Cortical source-based transfer learning accelerates brain-computer interface (BCI) training by using estimated brain activity. This method improves BCI classification accuracy and reduces calibration time, outperforming traditional approaches.

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Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Brain-computer interfaces (BCIs) offer rehabilitation potential for nervous system conditions.
BCI training is lengthy; transfer learning aims to accelerate this by reusing data.
Current transfer learning often ignores individual anatomy, limiting data utility.

Purpose of the Study:

To investigate cortical source-based transfer learning for BCI.
To improve BCI training efficiency and performance.
To address limitations of current transfer learning methods.

Main Methods:

Utilized source imaging to estimate cortical activity.
Transferred cortical activity estimates instead of scalp recordings.
Trained classifiers using exclusively cross-subject data.

Main Results:

Achieved classification accuracies comparable to or exceeding benchmark classifiers.
Demonstrated that performance depends on the number of transferred trials and cortical region.
Validated findings with simulated and measured electroencephalography (EEG) data.

Conclusions:

Cortical source-based transfer learning is a principled approach for data transfer.
This method enhances BCI classification performance.
Provides a viable strategy to reduce BCI calibration duration.