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Energy Losses in Transformers01:21

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Transformer-based hybrid systems to combat BCI illiteracy.

Maximilian Achim Pfeffer1, Johnny Kwok Wai Wong2, Sai Ho Ling1

  • 1Faculty of Engineering and Information Technology, University of Technology Sydney, New South Wales, Australia.

Computers in Biology and Medicine
|December 13, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances Brain-Computer Interfaces (BCIs) using hybrid Transformer and CNN models, significantly improving performance for users with low signal quality and BCI illiteracy.

Keywords:
Artificial intelligenceBCI illiteracyBiomedical engineeringBrain–computer-interfaceConvolutional neural networksElectroencephalographyHybrid-modelsNeural networksSignal processingTransformers

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Brain-Computer Interfaces (BCIs) face challenges with low signal-to-noise ratios and user-specific
  • BCI illiteracy
  • affecting up to 20% of users.
  • Transformer models show potential but are under-explored in BCI research.

Purpose of the Study:

  • To enhance Brain-Computer Interface (BCI) performance by developing and evaluating novel hybrid architectures.
  • To address limitations of low signal-to-noise ratios and improve classification accuracy for both strong and weak BCI learners.
  • To investigate the efficacy of integrating Convolutional Neural Networks (CNNs), Transformer blocks, and noise-infusion techniques.

Main Methods:

  • Experiment A: Assessed hybrid Convolutional and Transformer Block architectures for binary Motor Imagery (MI) classification.
  • Experiment B: Introduced a hybrid system with refined blocks and a Noise Focus Block for robust multi-class MI classification.
  • Experiment C: Evaluated architectures on 106 subjects, focusing on robustness across diverse user learning capabilities.

Main Results:

  • Experiment A achieved a validation accuracy of 0.914.
  • Experiment B's architecture improved multi-class MI classification to 84.5%, notably aiding BCI-illiterate users.
  • Experiment C demonstrated high robustness with Kappa >83% and a peak validation accuracy of 88.69% across all subjects.

Conclusions:

  • Hybrid integration of Transformers, CNNs, and noise-resonance layers significantly boosts BCI classification performance.
  • The proposed methods show particular benefit for weak BCI learners and users with low signal quality.
  • Further research is recommended for optimizing hybrid BCI architectures and hyperparameters to overcome existing performance limitations.