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Dual model transfer learning to compensate for individual variability in brain-computer interface.

Jun Su Kim1, HongJune Kim2, Chun Kee Chung3

  • 1Dept. of Brain and Cognitive Sciences, Seoul National University, Seoul, Republic of Korea; Clinical Research Institute, Konkuk University Medical Center Seoul, Republic of Korea.

Computer Methods and Programs in Biomedicine
|June 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel transfer learning method for brain-computer interfaces (BCI) that improves decoding performance by combining individual and group deep neural network models, effectively addressing subject-specific variability.

Keywords:
Brain-Computer Interface (BCI)DeepHeterogeneous dataIndividual variabilityNeural Network (DNN)Source estimationTransfer Learning

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Advancements in brain-computer interface (BCI) technology increasingly utilize deep neural networks (DNNs) for complex decoding tasks like arbitrary movement regression.
  • BCI models trained on individual data often suffer from limited performance and poor generalizability due to the high number of DNN parameters and extensive dataset requirements.
  • Group data may not yield sufficient decoding performance because of inherent variability in neural signals across individuals and over time.

Purpose of the Study:

  • To develop a transfer learning approach that effectively adapts to subject-specific variability in cortical regions for enhanced BCI performance.
  • To create a robust BCI decoding model that combines the strengths of individual and group data analysis.

Main Methods:

  • Trained separate movement decoding models on individual and pooled group data.
  • Generated salience maps from individual models to identify input contribution variance across subjects.
  • Combined individual and group models using a modified knowledge distillation framework, weighting universal applicability and individual fine-tuning.

Main Results:

  • The proposed combined model demonstrated superior decoding performance (mean r = 0.75) compared to individual (r = 0.70) and group models (r = 0.40) in arm-reaching tasks.
  • Significant performance improvements were observed in cases where individual models initially showed low decoding accuracy (e.g., from r = 0.50 to r = 0.61).
  • The method effectively encapsulated and adapted to individual neural signal variability.

Conclusions:

  • The developed transfer learning method shows significant potential for creating robust and broadly applicable brain-computer interfaces.
  • The approach successfully generalizes individual BCI data, enhancing decoding performance and overcoming limitations of traditional individual or group models.