ReviewMedical & biological engineering & computing2021
A novel method to reduce the motor imagery BCI illiteracy.
Review in Medical & biological engineering & computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Proficiency in motor imagery is linked to the lateralization of focused ERD patterns and beta PDC.Journal of neuroengineering and rehabilitation · 2025Article
- Musical auditory feedback BCI: clinical pilot study of the Encephalophone.Frontiers in human neuroscience · 2025Article
- Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity.Sensors (Basel, Switzerland) · 2023Article
- Bridging the BCI illiteracy gap: a subject-to-subject semantic style transfer for EEG-based motor imagery classification.Frontiers in human neuroscience · 2023Article
- Brain-Computer Interfaces for Upper Limb Motor Recovery after Stroke: Current Status and Development Prospects (Review).Sovremennye tekhnologii v meditsine · 2023Review
- Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
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Abstract
To reduce the motor imagery brain-computer interface (MI-BCI) illiteracy phenomenon and improve the classification accuracy, this paper proposed a novel method combining paradigm selection and Riemann distance classification. Firstly, a novel sensitivity-based paradigm selection (SPS) algorithm is designed for the optimization of classification to find the best classification pattern through a sensitive indicator. Then, a generalized Riemann minimum distance mean (GRMDM) classifier is proposed by introducing a weight factor to fuse the Log-Euclidean Metric classifier and the Riemannian Stein divergence classifier. The experimental results show that the proposed method achieves a better performance for multi-class motor imagery tasks. The average classification accuracy on the BCI competition IV dataset2a is 80.98%, which is 11.04% higher than Stein divergence classifier on the original two-class paradigm. Furthermore, the proposed method demonstrates its capacity on reducing MI-BCI illiteracy. Graphical abstract Here we investigate whether the BCI illiteracy phenomenon can be reduced through sensitivity-based paradigm selection (SPS) method and generalized Riemann minimum distance mean (GRMDM) classifier when performing motor imagery tasks.
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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.