ArticleIEEE transactions on human-machine systems2020
A Usability Study of Low-cost Wireless Brain-Computer Interface for Cursor Control Using Online Linear Model.
Article in IEEE transactions on human-machine systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Attention-Enhanced U-Net for Sensor-Efficient High-Density EEG Reconstruction in Wearable Brain Monitoring Systems.Journal of medical systems · 2026Article
- Non-Invasive Brain-Computer Interfaces: State of the Art and Trends.IEEE reviews in biomedical engineering · 2025Review
- Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach.Sensors (Basel, Switzerland) · 2024Article
- Evaluating the Feasibility of Visual Imagery for an EEG-Based Brain-Computer Interface.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
- EEGGAN-Net: enhancing EEG signal classification through data augmentation.Frontiers in human neuroscience · 2024Article
- Auditory-GAN: deep learning framework for improved auditory spatial attention detection.PeerJ. Computer science · 2024Article
- Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and usage of online BCI systems.Frontiers in human neuroscience · 2024Review
- Using principles of motor control to analyze performance of human machine interfaces.Scientific reports · 2023Article
- Using Principles of Motor Control to Analyze Performance of Human Machine Interfaces.Research square · 2023Article
- Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery.Cognitive neurodynamics · 2023Article
- Design of virtual BCI channels based on informer.Frontiers in human neuroscience · 2023Article
- Personalized Brain-Computer Interface and Its Applications.Journal of personalized medicine · 2022Review
- Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis.Sensors (Basel, Switzerland) · 2022Article
- Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?Frontiers in aging neuroscience · 2022Review
- [Human factors engineering of brain-computer interface and its applications: Human-centered brain-computer interface design and evaluation methodology].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Effects of Skin Friction on Tactile P300 Brain-Computer Interface Performance.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
Computer cursor control using electroencephalogram (EEG) signals is a common and well-studied brain-computer interface (BCI). The emphasis of the literature has been primarily on evaluation of the objective measures of assistive BCIs such as accuracy of the neural decoder whereas the subjective measures such as user's satisfaction play an essential role for the overall success of a BCI. As far as we know, the BCI literature lacks a comprehensive evaluation of the usability of the mind-controlled computer cursor in terms of decoder efficiency (accuracy), user experience, and relevant confounding variables concerning the platform for the public use. To fill this gap, we conducted a two-dimensional EEG-based cursor control experiment among 28 healthy participants. The computer cursor velocity was controlled by the imagery of hand movement using a paradigm presented in the literature named imagined body kinematics (IBK) with a low-cost wireless EEG headset. We evaluated the usability of the platform for different objective and subjective measures while we investigated the extent to which the training phase may influence the ultimate BCI outcome. We conducted pre- and post- BCI experiment interview questionnaires to evaluate the usability. Analyzing the questionnaires and the testing phase outcome shows a positive correlation between the individuals' ability of visualization and their level of mental controllability of the cursor. Despite individual differences, analyzing training data shows the significance of electrooculogram (EOG) on the predictability of the linear model. The results of this work may provide useful insights towards designing a personalized user-centered assistive BCI.
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Registered trials
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