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Working memory load recognition with deep learning time series classification.

Richong Pang1,2, Haojun Sang3, Li Yi4

  • 1Barco Technology Limited, Zhuhai 519031, China.

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Summary

This study introduces a deep learning model for decoding working memory load (WML) using fNIRS brain signals. The new TAResnet-BiLSTM model achieved 92.4% accuracy in inter-subject WML detection.

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Working memory load (WML) is a key signal in human-machine interaction.
  • Accurate WML evaluation is critical for effective applications.
  • Existing methods face challenges in inter-subject decoding.

Purpose of the Study:

  • To propose a deep learning (DL) time series classification (TSC) model for inter-subject WML decoding.
  • To evaluate the model's performance using functional near-infrared spectroscopy (fNIRS) data.
  • To advance brain-computer interface (BCI) applications for real-time WML detection.

Main Methods:

  • Collected fNIRS hemodynamic signals from 27 participants during visual working memory tasks.
  • Employed traditional machine learning (LDA, SVM) for intra-subject decoding.
  • Developed and applied a novel deep learning model, TAResnet-BiLSTM, for inter-subject decoding.

Main Results:

  • Intra-subject classification accuracy reached 94.6% (LDA) and 79.1% (SVM).
  • The proposed TAResnet-BiLSTM model achieved a high inter-subject WML decoding accuracy of 92.4%.
  • Demonstrated superior performance of the DL model in cross-participant WML decoding.

Conclusions:

  • The TAResnet-BiLSTM model offers a promising approach for inter-subject WML decoding.
  • fNIRS combined with DL provides a viable method for real-time WML detection in BCI.
  • This research opens new avenues for brain-computer interfaces in cognitive load assessment.