ArticleFrontiers in neuroscience2021
Exploring Fatigue Effects on Performance Variation of Intensive Brain-Computer Interface Practice.
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 12 papers, 3 of them syntheses that pooled it.
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12 citing papers in PubMed, 3 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Dataset Fragmentation, Cognitive Variability, and Reproducibility Challenges in EEG-Based Brain-Computer Interfaces: A PRISMA-Based Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Influence of age, cognitive function, attention, and mental state on the effectiveness of EEG-based brain-computer interface device use: a systematic review.Journal of neuroengineering and rehabilitation · 2025Pooled it
- The effect of rehabilitation training based on brain-computer interface on limb function in stroke patients: a systematic review and meta-analyses.Frontiers in neurologyPooled it
- Brain-computer interfaces: innovations and challenges in the field of cerebral palsy rehabilitation.Italian journal of pediatrics · 2026Review
- Exploration-based feedback for BCI training: a case study with an adolescent with paraplegia.Journal of neuroengineering and rehabilitation · 2026Article
- Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training.PLoS computational biology · 2026Article
- A hybrid EMG-EEG interface for robust intention detection and fatigue-adaptive control of an elbow rehabilitation robot.Scientific reports · 2025Article
- The Effect of Caffeine on Movement-Related Cortical Potential Morphology and Detection.Sensors (Basel, Switzerland) · 2024Article
- Fatigue in children using motor imagery and P300 brain-computer interfaces.Journal of neuroengineering and rehabilitation · 2024Article
- Considerations and discussions on the clear definition and definite scope of brain-computer interfaces.Frontiers in neuroscience · 2024Review
- [The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer's disease].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Identifying Amnestic Mild Cognitive Impairment With Convolutional Neural Network Adapted to the Spectral Entropy Heat Map of the Electroencephalogram.Frontiers in human neuroscience · 2022Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motor imagery (MI) is an endogenous mental process and is commonly used as an electroencephalogram (EEG)-based brain-computer interface (BCI) strategy. Previous studies of P300 and MI-based (without online feedback) BCI have shown that mental states like fatigue can negatively affect participants' EEG signatures. However, exogenous stimuli cause visual fatigue, which might have a different mechanism than endogenous tasks do. Furthermore, subjects could adjust themselves if online feedback is provided. In this sense, it is still unclear how fatigue affects online MI-based BCI performance. With this question, 12 healthy subjects are recruited to investigate this issue, and an MI-based online BCI experiment is performed for four sessions on different days. The first session is for training, and the other three sessions differ in rest condition and duration-no rest, 16-min eyes-open rest, and 16-min eyes-closed rest-arranged in a pseudo-random order. Multidimensional fatigue inventory (MFI) and short stress state questionnaire (SSSQ) reveal that general fatigue, mental fatigue, and distress have increased, while engagement has decreased significantly within certain sessions. However, the BCI performances, including percent valid correct (PVC) and information transfer rate (ITR), show no significant change across 400 trials. The results suggest that although the repetitive MI task has affected subjects' mental states, their BCI performances and feature separability within a session are not affected by the task significantly. Further electrophysiological analysis reveals that the alpha-band power in the sensorimotor area has an increasing tendency, while event-related desynchronization (ERD) modulation level has a decreasing trend. During the rest time, no physiological difference has been found in the eyes-open rest condition; on the contrary, the alpha-band power increase and subsequent decrease appear in the eyes-closed rest condition. In summary, this experiment shows evidence that mental states can change dramatically in the intensive MI-BCI practice, but BCI performances could be maintained.
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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.