ArticleMicromachines2022
Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network.
Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Transfer learning for subject-independent motor imagery EEG classification using convolutional relational networks.Frontiers in neuroscience · 2025Article
- Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.Journal of neuroengineering and rehabilitation · 2024Article
- Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding.Frontiers in neuroscience · 2023Article
- Survey on the research direction of EEG-based signal processing.Frontiers in neuroscience · 2023Review
- Deep Convolutional Neural Network for EEG-Based Motor Decoding.Micromachines · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
For the successful application of brain-computer interface (BCI) systems, accurate recognition of electroencephalography (EEG) signals is one of the core issues. To solve the differences in individual EEG signals and the problem of less EEG data in classification and recognition, an attention mechanism-based multi-scale convolution network was designed; the transfer learning data alignment algorithm was then introduced to explore the application of transfer learning for analyzing motor imagery EEG signals. The data set 2a of BCI Competition IV was used to verify the designed dual channel attention module migration alignment with convolution neural network (MS-AFM). Experimental results showed that the classification recognition rate improved with the addition of the alignment algorithm and adaptive adjustment in transfer learning; the average classification recognition rate of nine subjects was 86.03%.
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Registered trials
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.