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Deep and Wide Transfer Learning with Kernel Matching for Pooling Data from Electroencephalography and Psychological

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Summary

This study introduces a novel cross-subject transfer learning method to enhance motor imagery (MI) brain-computer interface (BCI) performance for inefficient users. The approach uses a Deep and Wide neural network and questionnaire data to improve EEG decoding accuracy.

Keywords:
Deep and Wide networkkernel-embeddingmotor imagerytransfer learning

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Motor imagery (MI) is crucial for motor learning and brain-computer interface (BCI) systems using electroencephalogram (EEG) decoding.
  • Inefficiency in MI self-regulation necessitates longer training periods for BCI users.
  • Improving BCI performance for individuals with poor MI skills is a significant challenge.

Purpose of the Study:

  • To develop a parameter-based cross-subject transfer learning approach to enhance MI-BCI performance.
  • To improve the accuracy of BCI systems for individuals with MI inefficiency.
  • To integrate EEG data with psychological questionnaire data for improved BCI performance.

Main Methods:

  • Implemented a Deep and Wide neural network for MI classification and pre-training.
  • Utilized a fine-tuning procedure to transfer network parameters to a target network.
  • Employed stepwise kernel-matching via Gaussian-embedding for data fusion of categorical and real-valued features.
  • Applied inefficiency-based clustering for subject selection and evaluation of source-target sets.

Main Results:

  • The proposed Deep and Wide neural network demonstrated competitive accuracy in MI classification.
  • Transfer learning effectively improved performance for individuals with MI inefficiency.
  • Integration of questionnaire data alongside EEG data did not compromise accuracy.

Conclusions:

  • The parameter-based cross-subject transfer learning approach is effective in enhancing MI-BCI performance.
  • This method offers a promising solution for users with MI inefficiency.
  • The integration of diverse data sources, including psychological data, can improve BCI system robustness.