ArticleFrontiers in neuroscience2023
Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline 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
- Transformer-Based Modeling of Directed Transfer Entropy Connectivity for EEG-Based ADHD Classification in Children.Sensors (Basel, Switzerland) · 2026Article
- Toward Robust EEG Classification Using Adaptive CNN-Transformer and Inception Architectures and Signal Level Augmentation.Sensors (Basel, Switzerland) · 2026Article
- N2G calibrator: a cross-subject adversarial learning framework for neural signal-driven gait tracking in Parkinson's disease.Communications engineering · 2026Article
- Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition.Scientific reports · 2026Article
- Cortical beta coherence provides a stronger non-invasive predictor of movement vigor than local beta power.Journal of neuroengineering and rehabilitation · 2026Article
- Joint MVMD-based optimal feature selection and FW-LS-TWSVM for motor imagery recognition.Scientific reports · 2026Article
- Virtual reality mediated brain-computer interface training improves sensorimotor neuromodulation in unimpaired and post spinal cord injury individuals.Scientific reports · 2026Article
- Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.Scientific reports · 2026Article
- Takens-Based Kernel Transfer Entropy Connectivity Network for Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- Adaptive EEG preprocessing to mitigate electrode shift variability for robust motor imagery classification.Scientific reports · 2025Article
- A time-frequency feature fusion-based deep learning network for SSVEP frequency recognition.Frontiers in neuroscience · 2025Article
- AM-MTEEG: multi-task EEG classification based on impulsive associative memory.Frontiers in neuroscience · 2025Article
- Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA.IEEE journal of translational engineering in health and medicine · 2025Article
- Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients.Scientific reports · 2024Article
- Image classification and reconstruction from low-density EEG.Scientific reports · 2024Article
- Advancing stroke rehabilitation: the potential and challenges of closed-loop brain-computer interface technology.Frontiers in neurologyReview
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10 authors.
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Abstract
Introduction: Inter- and intra-subject variability are caused by the variability of the psychological and neurophysiological factors over time and across subjects. In the application of in Brain-Computer Interfaces (BCI), the existence of inter- and intra-subject variability reduced the generalization ability of machine learning models seriously, which further limited the use of BCI in real life. Although many transfer learning methods can compensate for the inter- and intra-subject variability to some extent, there is still a lack of clear understanding about the change of feature distribution between the cross-subject and cross-session electroencephalography (EEG) signal. Methods: To investigate this issue, an online platform for motor-imagery BCI decoding has been built in this work. The EEG signal from both the multi-subject (Exp1) and multi-session (Exp2) experiments has been analyzed from multiple perspectives. Results: Firstly we found that with the similar variability of classification results, the time-frequency response of the EEG signal within-subject in Exp2 is more consistent than cross-subject results in Exp1. Secondly, the standard deviation of the common spatial pattern (CSP) feature has a significant difference between Exp1 and Exp2. Thirdly, for model training, different strategies for the training sample selection should be applied for the cross-subject and cross-session tasks. Discussion: All these findings have deepened the understanding of inter- and intra-subject variability. They can also guide practice for the new transfer learning methods development in EEG-based BCI. In addition, these results also proved that BCI inefficiency was not caused by the subject's unable to generate the event-related desynchronization/synchronization (ERD/ERS) signal during the motor imagery.
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