ArticleFrontiers in neuroscience2021
Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 27 citations in OpenAlex.
- Comprehensive benchmarking and explainable machine learning analysis of EEG imagery activity recognition.Scientific reports · 2026Article
- Lower-Limb Motor Imagery Recognition Prototype Based on EEG Acquisition, Filtering, and Machine Learning-Based Pattern Detection.Sensors (Basel, Switzerland) · 2025Article
- Improved filter bank common spatial pattern algorithm based on the sparrow search algorithm.Frontiers in human neuroscience · 2025Article
- Deep learning networks based decision fusion model of EEG and fNIRS for classification of cognitive tasks.Cognitive neurodynamics · 2024Article
- Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism.Medical & biological engineering & computing · 2023Article
- An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets.Sensors (Basel, Switzerland) · 2022Article
- A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm.Frontiers in neuroscience · 2022Article
- Research on Rehabilitation Training Strategies Using Multimodal Virtual Scene Stimulation.Frontiers in aging neuroscience · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and classification have the problems of low accuracy and efficiency. To solve this problem, this paper proposes a recognition method of motor imagery EEG signal based on deep convolution network. This method firstly aims at the problem of low quality of EEG signal characteristic data, and uses short-time Fourier transform (STFT) and continuous Morlet wavelet transform (CMWT) to preprocess the collected experimental data sets based on time series characteristics. So as to obtain EEG signals that are distinct and have time-frequency characteristics. And based on the improved CNN network model to efficiently recognize EEG signals, to achieve high-quality EEG feature extraction and classification. Further improve the quality of EEG signal feature acquisition, and ensure the high accuracy and precision of EEG signal recognition. Finally, the proposed method is validated based on the BCI competiton dataset and laboratory measured data. Experimental results show that the accuracy of this method for EEG signal recognition is 0.9324, the precision is 0.9653, and the AUC is 0.9464. It shows good practicality and applicability.
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