ArticleFrontiers in neuroinformatics2022
Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 26 citations in OpenAlex.
- Improving motor imagery decoding methods for an EEG-based mobile brain-computer interface in the context of the 2024 Cybathlon.Journal of neuroengineering and rehabilitation · 2026Article
- EEG-based dataset explicitly targets the transitions between sitting and standing for exploring neural activation patterns in motor imagery and execution.GigaScience · 2026Article
- P300 ERP System Utilizing Wireless Visual Stimulus Presentation Devices.Sensors (Basel, Switzerland) · 2025Article
- Towards neuroadaptive chatbots: a feasibility study.Frontiers in neuroergonomics · 2025Article
- Improved filter bank common spatial pattern algorithm based on the sparrow search algorithm.Frontiers in human neuroscience · 2025Article
- Article
- Enhanced lower-limb motor imagery by kinesthetic illusion.Frontiers in neuroscience · 2023Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
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Abstract
Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.
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