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A Usability Study of Low-cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model.

Reza Abiri¹, Soheil Borhani², Justin Kilmarx²

¹Dept. of Neurology at University of California, San Francisco/Berkeley and Dept. of Mechanical, Aerospace, and Biomedical Engineering at the University of Tennessee, Knoxville.

IEEE Transactions on Human-Machine Systems

|March 29, 2021

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This summary is machine-generated.

This study explored brain-computer interface (BCI) usability, finding a link between visualization ability and cursor control. Electroencephalogram (EEG) data revealed electrooculogram (EOG) significance in training for personalized BCI design.

Area of Science:

Neuroscience
Human-Computer Interaction
Biomedical Engineering

Background:

Brain-computer interfaces (BCIs) commonly use electroencephalogram (EEG) for cursor control, focusing on objective accuracy.
Subjective user satisfaction is crucial for BCI success but often overlooked in research.
A comprehensive usability evaluation of EEG-based cursor control, considering user experience and confounding factors, is lacking.

Purpose of the Study:

To comprehensively evaluate the usability of an EEG-based computer cursor control system.
To investigate the correlation between objective performance (decoder efficiency) and subjective user experience.
To explore the influence of training and individual differences on BCI outcomes.

Main Methods:

Conducted a 2D EEG-based cursor control experiment with 28 healthy participants using the imagined body kinematics (IBK) paradigm.

Keywords:

Brain-Computer Interface Confounding variables Cursor control EEG Imagined Body Kinematics Usability

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Utilized a low-cost wireless EEG headset for data acquisition.

Administered pre- and post-experiment questionnaires to assess usability and user satisfaction.

Main Results:

A positive correlation was observed between participants' visualization ability and their mental cursor controllability.
Individual differences in BCI performance were noted.
Analysis of training data highlighted the significant impact of electrooculogram (EOG) signals on linear model predictability.

Conclusions:

User-centered design is essential for effective assistive BCIs.
Individual visualization skills are a key factor in EEG-based cursor control performance.
EOG artifacts during training can influence the predictability of EEG-based BCI models, necessitating careful consideration in personalized BCI development.