ArticleScientific reports2020
Group task-related component analysis (gTRCA): a multivariate method for inter-trial reproducibility and inter-subject similarity maximization for EEG data analysis.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- EEG analysis of brain dynamics in a simulated multi-task and multi-stage learning environment.NPJ science of learning · 2025Article
- Forewarned Is Forearmed: The Single- and Dual-Brain Mechanisms in Detectors from Dyads of Varying Social Distance during Deceptive Outcome Evaluation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Filter bank temporally delayed CCA for uncalibrated SSVEP-BCI.Medical & biological engineering & computing · 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
- Time-frequency feature extraction based on multivariable synchronization index for training-free SSVEP-based BCI.Cognitive neurodynamics · 2024Article
- cVEP Training Data Validation-Towards Optimal Training Set Composition from Multi-Day Data.Brain sciences · 2022Article
- Facilitating Applications of SSVEP-Based BCIs by Within-Subject Information Transfer.Frontiers in neuroscience · 2022Article
- Detection of Brain Network Communities During Natural Speech Comprehension From Functionally Aligned EEG Sources.Frontiers in computational neuroscience · 2022Article
- Deep Feature MiningFrontiers in bioengineering and biotechnology · 2021Article
- Application of Transfer Learning in EEG Decoding Based on Brain-Computer Interfaces: A Review.Sensors (Basel, Switzerland) · 2020Review
- Extracting reproducible components from electroencephalographic responses to transcranial magnetic stimulation with group task-related component analysis.Imaging neuroscience (Cambridge, Mass.)Article
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1 author.
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Abstract
EEG is known to contain considerable inter-trial and inter-subject variability, which poses a challenge in any group-level EEG analyses. A true experimental effect must be reproducible even with variabilities in trials, sessions, and subjects. Extracting components that are reproducible across trials and subjects benefits both understanding common mechanisms in neural processing of cognitive functions and building robust brain-computer interfaces. This study extends our previous method (task-related component analysis, TRCA) by maximizing not only trial-by-trial reproducibility within single subjects but also similarity across a group of subjects, hence referred to as group TRCA (gTRCA). The problem of maximizing reproducibility of time series across trials and subjects is formulated as a generalized eigenvalue problem. We applied gTRCA to EEG data recorded from 35 subjects during a steady-state visual-evoked potential (SSVEP) experiment. The results revealed: (1) The group-representative data computed by gTRCA showed higher and consistent spectral peaks than other conventional methods; (2) Scalp maps obtained by gTRCA showed estimated source locations consistently within the occipital lobe; And (3) the high-dimensional features extracted by gTRCA are consistently mapped to a low-dimensional space. We conclude that gTRCA offers a framework for group-level EEG data analysis and brain-computer interfaces alternative in complement to grand averaging.
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