ArticleCognitive neurodynamics2017
Regularized common spatial patterns with subject-to-subject transfer of EEG signals.
Article in Cognitive neurodynamics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.Biomimetics (Basel, Switzerland) · 2026Article
- A novel dual-step transfer framework based on domain selection and feature alignment for motor imagery decoding.Cognitive neurodynamics · 2024Article
- Time-frequency-space transformer EEG decoding for spinal cord injury.Cognitive neurodynamics · 2024Article
- Identifying optimal channels and features for multi-participant motor imagery experiments across a participant's multi-day multi-class EEG data.Cognitive neurodynamics · 2024Article
- EMPT: a sparsity Transformer for EEG-based motor imagery recognition.Frontiers in neuroscience · 2024Article
- Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives.Frontiers in neuroscience · 2023Article
- Supervised and Semisupervised Manifold Embedded Knowledge Transfer in Motor Imagery-Based BCI.Computational intelligence and neuroscience · 2022Article
- Empirical comparison of deep learning methods for EEG decoding.Frontiers in neuroscience · 2022Article
- Decoding subjective emotional arousal from EEG during an immersive virtual reality experience.eLife · 2021Article
- Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm.Cognitive neurodynamics · 2021Article
- Selective Cross-Subject Transfer Learning Based on Riemannian Tangent Space for Motor Imagery Brain-Computer Interface.Frontiers in neuroscience · 2021Article
- A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain-Computer Interface.Frontiers in neuroscience · 2021Review
- OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals.PeerJ. Computer science · 2021Article
- A brain-computer interface for the continuous, real-time monitoring of working memory load in real-world environments.Cognitive neurodynamics · 2020Article
- A cognitive brain-computer interface monitoring sustained attentional variations during a continuous task.Cognitive neurodynamics · 2019Article
- Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems : Guidelines Derived from Simulation and Real-World Data.Neuroinformatics · 2019Article
- Across-subject offline decoding of motor imagery from MEG and EEG.Scientific reports · 2018Article
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3 authors.
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Abstract
In the context of brain-computer interface (BCI) system, the common spatial patterns (CSP) method has been used to extract discriminative spatial filters for the classification of electroencephalogram (EEG) signals. However, the classification performance of CSP typically deteriorates when a few training samples are collected from a new BCI user. In this paper, we propose an approach that maintains or improves the recognition accuracy of the system with only a small size of training data set. The proposed approach is formulated by regularizing the classical CSP technique with the strategy of transfer learning. Specifically, we incorporate into the CSP analysis inter-subject information involving the same task, by minimizing the difference between the inter-subject features. Experimental results on two data sets from BCI competitions show that the proposed approach greatly improves the classification performance over that of the conventional CSP method; the transformed variant proved to be successful in almost every case, based on a small number of available training samples.
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