ArticleFrontiers in neuroscience2020
Improving Real-Time Brain State Classification of Motor Imagery Tasks During Neurofeedback Training.
Article in Frontiers in neuroscience, 2020. 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.
- Recommendations for Combining Brain-Computer Interface, Motor Imagery, and Virtual Reality in Upper Limb Stroke Rehabilitation: Qualitative Participatory Design Study.JMIR rehabilitation and assistive technologies · 2025Article
- Regulation of craving for real-time fMRI neurofeedback based on individual classification.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Semantic fMRI neurofeedback of emotions: from basic principles to clinical applications.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Evaluating EEG neurofeedback in sport psychology: a systematic review of RCT studies for insights into mechanisms and performance improvement.Frontiers in psychology · 2024Review
- Exploring protocol development: Implementing systematic contextual memory to enhance real-time fMRI neurofeedback.Journal of electrical bioimpedance · 2024Article
- Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks.Frontiers in neuroscience · 2022Article
- Research on Rehabilitation Training Strategies Using Multimodal Virtual Scene Stimulation.Frontiers in aging neuroscience · 2022Article
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Authors and funding
5 authors.
Funding
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Abstract
In this study, we investigated the effect of the dynamic changes in brain activation during neurofeedback training in the classification of the different brain states associated with the target tasks. We hypothesized that ongoing activation patterns could change during neurofeedback session due to learning effects and, in the process, could affect the performance of brain state classifiers trained using data obtained prior to the session. Using a motor imagery paradigm, we then examined the application of an incremental training approach where classifiers were continuously updated in order to account for these activation changes. Our results confirmed our hypothesis that neurofeedback training could be associated with dynamic changes in brain activation characterized by an initially more widespread brain activation followed by a more focused and localized activation pattern. By continuously updating the trained classifiers after each feedback run, significant improvement in accurately classifying the different brain states associated with the target motor imagery tasks was achieved. These findings suggest the importance of taking into account brain activation changes during neurofeedback in order to provide more reliable and accurate feedback information to the participants, which is critical for an effective neurofeedback application.
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