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Segment alignment based cross-subject motor imagery classification under fading data.

Zitong Wan1, Rui Yang2, Mengjie Huang3

  • 1Design School, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China; Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3BX, United Kingdom.

Computers in Biology and Medicine
|November 10, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel cross-subject approach to classify motor imagery (MI) signals, addressing challenges like personalization and data fading. The method effectively classifies fading MI data from single subjects using models trained on multi-subject normal data.

Keywords:
Artificial intelligenceCross subjectData fadingMotor imagerySegment alignmentTransfer learning

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Brain-Computer Interfaces

Background:

  • Motor imagery (MI) utilizes brain imagination for motor learning, with machine learning algorithms like Common Spatial Patterns (CSP) aiding MI signal analysis.
  • Conventional MI signal analysis faces challenges in feature extraction and recognition, specifically high personalization across subjects and data fading, which degrades signal quality.
  • Data fading is a newly identified issue impacting MI signal integrity, hindering reliable classification and requiring novel solutions.

Purpose of the Study:

  • To propose a cross-subject fading data classification approach to address personalization and data fading in motor imagery (MI) signal analysis.
  • To enable classification of fading MI data from a single target subject using a model trained on normal data from multiple source subjects.
  • To enhance the robustness and accuracy of MI-based brain-computer interfaces (BCIs) despite signal quality variations.

Main Methods:

  • A novel cross-subject fading data classification approach incorporating segment alignment is developed.
  • The proposed method trains a classification model using normal MI data from multiple subjects.
  • The trained model is then applied to classify fading MI data from a single target subject, even under varying fading levels.

Main Results:

  • The proposed method demonstrated effective classification performance across different subjects and varying levels of data fading.
  • Experimental validation using a BCI Competition dataset and a custom lab-based experiment confirmed the method's efficacy.
  • The approach successfully overcomes the limitations of high personalization and data fading in MI signal analysis.

Conclusions:

  • The developed cross-subject fading data classification approach with segment alignment effectively addresses key challenges in motor imagery signal processing.
  • This method offers a robust solution for classifying fading MI data, improving the reliability of BCIs across diverse users and signal conditions.
  • The findings pave the way for more personalized and resilient brain-computer interface applications by mitigating data fading and subject-specific variations.