ArticleCognitive neurodynamics2022
A multi-modal brain-computer interface based on threshold discrimination and its application in wheelchair control.
Article in Cognitive neurodynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Brain-computer interfaces: innovations and challenges in the field of cerebral palsy rehabilitation.Italian journal of pediatrics · 2026Review
- An Immersive P300 Brain-Computer Interface Based on 3D Morphological Stimuli and Self-Adaptive Bayesian Linear Discriminant Analysis.Biomimetics (Basel, Switzerland) · 2026Article
- Brain-Computer Interfaces for Stroke Motor Rehabilitation.Bioengineering (Basel, Switzerland) · 2025Review
- Effect of 3D paradigm synchronous motion for SSVEP-based hybrid BCI-VR system.Medical & biological engineering & computing · 2023Article
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Authors and funding
5 authors.
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Abstract
In this study, we propose a novel multi-modal brain-computer interface (BCI) system based on the threshold discrimination, which is proposed for the first time to distinguish between SSVEP and MI potentials. The system combines these two heterogeneous signals to increase the number of control commands and improve the performance of asynchronous control of external devices. In this research, an electric wheelchair is controlled as an example. The user can continuously control the wheelchair to turn left/right through motion imagination (MI) by imagining left/right-hand movement and generate another 6 commands for the wheelchair control by focusing on the SSVEP stimulation panel. Ten subjects participated in a MI training session and eight of them completed a mobile obstacle-avoidance experiment in a complex environment requesting high control accuracy for successful manipulation. Comparing with the single-modal BCI-controlled wheelchair system, the results demonstrate that the proposed multi-modal method is effective by providing more satisfactory control accuracy, and show the potential of BCI-controlled systems to be applied in complex daily tasks.
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