ArticleHeliyon2023
Improvement of brain-computer interface in motor imagery training through the designing of a dynamic experiment and FBCSP.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 21 citations in OpenAlex.
- A Comprehensive Review of Sequence and Generative Models in Motor Imagery (MI) Classification for Brain-Computer Interfaces (BCIs).Sensors (Basel, Switzerland) · 2026Review
- Motor imagery BCI enables more practical and user-friendly exoskeleton control than smartwatch for users with spinal cord injury: a preliminary study.Journal of neuroengineering and rehabilitation · 2026Article
- Motor Imagery Acquisition Paradigms: In the Search to Improve Classification Accuracy.Sensors (Basel, Switzerland) · 2025Article
- A Personalized Multimodal BCI-Soft Robotics System for Rehabilitating Upper Limb Function in Chronic Stroke Patients.Biomimetics (Basel, Switzerland) · 2025Article
- A bimodal deep learning network based on CNN for fine motor imagery.Cognitive neurodynamics · 2024Article
- Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN.Heliyon · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motor imagery (MI) can produce a specific brain pattern when the subject imagines performing a particular action without any actual body movements. According to related previous research, the improvement of the training of MI brainwaves can be adopted by feedback methods in which the analysis of brainwave characteristics is very important. The aim of this study was to improve the subject's MI and the accuracy of classification. In order to ameliorate the accuracy of the MI of the left and right hand, the present study designed static and dynamic visual stimuli in experiments so as to evaluate which one can improve subjects' imagination training. Additionally, the filter bank common spatial pattern (FBCSP) method was used to divide the frequency band range of the brainwaves into multiple segments, following which linear discriminant analysis (LDA) was adopted for classification. The results revealed that the averaged false positive rate (FPR) under FBCSP-LDA in the dynamic MI experiment was the lowest FPR (23.76%). As such, this study suggested that a combination of the dynamic MI experiment and the FBCSP-LDA method improved the overall prediction error rate and ameliorated the performance of the MI brain-computer interface.
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Registered trials
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.