ArticleFrontiers in neuroscience2021
Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 33 citations in OpenAlex.
- Motor imagery enhances performance beyond the imagined action.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The Application of Entropy in Motor Imagery Paradigms of Brain-Computer Interfaces.Brain sciences · 2025Review
- Improved filter bank common spatial pattern algorithm based on the sparrow search algorithm.Frontiers in human neuroscience · 2025Article
- Stochasticity as a solution for overfitting-A new model and comparative study on non-invasive EEG prospects.Frontiers in human neuroscience · 2025Article
- Neural signatures of motor imagery for a supernumerary thumb in VR: an EEG analysis.Scientific reports · 2024Article
- Motor imagery classification method based on relative wavelet packet entropy brain network and improved lasso.Frontiers in neuroscience · 2023Article
- Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills.Sensors (Basel, Switzerland) · 2022Article
- Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data.Neurophotonics · 2022Article
- The Effects of Sensory Threshold Somatosensory Electrical Stimulation on Users With Different MI-BCI Performance.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The motor imagery (MI)-based brain-computer interface (BCI) is an intuitive interface that provides control over computer applications directly from brain activity. However, it has shown poor performance compared to other BCI systems such as P300 and SSVEP BCI. Thus, this study aimed to improve MI-BCI performance by training participants in MI with the help of sensory inputs from tangible objects (i.e., hard and rough balls), with a focus on poorly performing users. The proposed method is a hybrid of training and imagery, combining motor execution and somatosensory sensation from a ball-type stimulus. Fourteen healthy participants participated in the somatosensory-motor imagery (SMI) experiments (within-subject design) involving EEG data classification with a three-class system (signaling with left hand, right hand, or right foot). In the scenario of controlling a remote robot to move it to the target point, the participants performed MI when faced with a three-way intersection. The SMI condition had a better classification performance than did the MI condition, achieving a 68.88% classification performance averaged over all participants, which was 6.59% larger than that in the MI condition (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.