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Exploration of User's Mental State Changes during Performing Brain-Computer Interface.

Li-Wei Ko1,2,3,4, Rupesh Kumar Chikara1,2, Yi-Chieh Lee5

  • 1Department of Biological Science and Technology, College of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan.

Sensors (Basel, Switzerland)
|June 7, 2020
PubMed
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This summary is machine-generated.

This study introduces a new brain-computer interface (BCI) experiment combining working memory tasks with steady-state visual evoked potential (SSVEP) to detect user cognitive states. Findings reveal distinct EEG patterns differentiating mental focus from lost-in-thought states, improving BCI performance.

Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) show promise but are sensitive to user cognitive state changes.
  • Recognizing mental focus versus lost-in-thought states is crucial for enhancing sustained SSVEP-BCI performance.
  • Existing methods struggle to identify user mental state fluctuations during SSVEP-BCI tasks.

Purpose of the Study:

  • To develop and validate a novel BCI experimental paradigm integrating a working memory task with SSVEP.
  • To differentiate users' cognitive states (mental focus vs. lost-in-thought) by analyzing neural activity changes.
  • To establish new neural markers for recognizing cognitive states in SSVEP-BCI users.

Main Methods:

Keywords:
brain–computer interface (BCI)electroencephalography (EEG)lost-in-thought statemental focus statesteady-state visual evoked potential (SSVEP)working memory

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  • Designed a BCI experiment combining a working memory task with SSVEP stimuli at 12 Hz or 30 Hz.
  • Analyzed electroencephalogram (EEG) activity, focusing on delta, theta, alpha, and beta frequency bands.
  • Employed K-Nearest Neighbors (KNN) and Bayesian network classifiers to assess cognitive state classification accuracy.

    • Main Results:

    • Significant increases in delta, theta, and beta EEG activity were observed in the frontal lobe during mental focus compared to lost-in-thought states.
    • Delta, alpha, and beta band powers were higher in the occipital lobe during mental focus than in the lost-in-thought state.
    • Average classification performance for cognitive states reached 77% to 80% using KNN and Bayesian network classifiers.

    Conclusions:

    • Cognitive state fluctuations significantly impact SSVEP-BCI performance.
    • The developed experimental scenario effectively recognizes users' cognitive states during BCI tasks.
    • Identified EEG patterns serve as novel neural markers for future BCI development and cognitive state monitoring.