ArticleFrontiers in computational neuroscience2022
Analysis of instantaneous brain interactions contribution to a motor imagery classification task.
Article in Frontiers in computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A feature fusion network with spatial-temporal-enhanced strategy for the motor imagery of force intensity variation.Frontiers in neuroscience · 2025Article
- A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women.Frontiers in computational neuroscience · 2024Article
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Authors and funding
3 authors.
Funding
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Abstract
The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear discriminant analysis, LDA) model and a dynamic (hidden conditional random field, HCRF) model. The impact of using the sliding window technique (SWT) in the static and dynamic models is also analyzed. The study proved that their combination with temporal features provides significant information to improve the classification in a two-class motor imagery task for LDA (average accuracy: 0.7192 no additional features, 0.7617 by adding correlation, 0.7606 by adding Jaccard distance;
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