ReviewFrontiers in computational neuroscience2019
Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review.
Review in Frontiers in computational neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
83 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Dataset Fragmentation, Cognitive Variability, and Reproducibility Challenges in EEG-Based Brain-Computer Interfaces: A PRISMA-Based Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Brain-computer interface for upper limb functional recovery post-stroke: a meta-analysis of improvement in upper limb motor function and changes in neurophysiological markers.Journal of neuroengineering and rehabilitation · 2026Pooled it
- How different immersive environments affect intracortical brain computer interfaces.Journal of neural engineering · 2025Trial
- A novel therapeutic regimen based on the constrained-disorder principles may improve the response to dimethyl fumarate in multiple sclerosis: Findings from an exploratory feasibility open-label clinical trial.IBRO neuroscience reports · 2026Article
- Task-Oriented Semantic Feature Transmission for Robust EEG Motor Imagery Decoding Under Additive White Gaussian Noise.Sensors (Basel, Switzerland) · 2026Article
- Optimization of attention mechanisms in choral conducting education using EEG and eye movement tracking with transfer learning algorithms.Scientific reports · 2026Article
- N2G calibrator: a cross-subject adversarial learning framework for neural signal-driven gait tracking in Parkinson's disease.Communications engineering · 2026Article
- Continual-Learning-Enhanced CNN-Transformer Framework for Real-Time Motor-Imagery BCI in Virtual Environments.Bioengineering (Basel, Switzerland) · 2026Article
- MDEFusion: A Multi-Domain EEG Feature Fusion Network With Bidirectional Attention LSTM and Half-Split Crossover SAE for Schizophrenia Recognition.Brain and behavior · 2026Article
- A Neurophysiological Stratification Framework for Intermediate Motor Imagery-BCI Users Based on Independent Event-Related Brain Dynamics.Brain sciences · 2026Article
- Variability in Exercise is Linked to Improved Age-related Dysfunctions, Suggesting a Potential Role for the Constrained-Disorder Principle-based Second-Generation Artificial Intelligence System.Current aging science · 2026Article
- Render EEG-Based Brain-Computer Interfaces Calibration-Free: Trade Space for Time in EEG Decoding.IEEE open journal of engineering in medicine and biology · 2026Article
- Individualized brain-computer interface for people with disabilities: a review.Frontiers in human neuroscience · 2026Review
- Bridging cognition and control through passive eye movement integration in motor imagery brain-computer interfaces.Frontiers in human neuroscience · 2026Review
- A dynamic multi-branch EEG decoding network for motor imagery classification with preliminary clinical validation.Frontiers in neuroscience · 2026Article
- Single-subject auditory ERP-BCI performance enhancement in ALS via an AI coding assistant prompt.Frontiers in human neuroscience · 2026Article
- Gaussian Connectivity-Driven EEG Imaging for Deep Learning-Based Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- Brain-inspired signal processing for detecting stress during mental arithmetic tasks.Brain informatics · 2025Article
- Ethical imperatives in the commercialization of brain-computer interfaces.IBRO neuroscience reports · 2025Article
- Intersession Robust Hybrid Brain-Computer Interface: Safe and User-Friendly Approach with LED Activation Mechanism.Micromachines · 2025Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra- and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.
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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.