ArticleFrontiers in human neuroscience2020
A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals.
Article in Frontiers in human neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- Evaluating Validation Strategies in Motor Imagery EEG: A Full-Cohort GAF-PLV Analysis and Matched Sensitivity Study.Brain sciences · 2026Article
- Classification of Electroencephalography Motor Execution Signals Using a Hybrid Neural Network Based on Instantaneous Frequency and Amplitude Obtained via Empirical Wavelet Transform.Sensors (Basel, Switzerland) · 2025Article
- Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.Scientific reports · 2025Article
- Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- Spatial Cognitive EEG Feature Extraction and Classification Based on MSSECNN and PCMI.Bioengineering (Basel, Switzerland) · 2024Article
- Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.Brain sciences · 2024Article
- Improving inter-session performance via relevant session-transfer for multi-session motor imagery classification.Heliyon · 2024Article
- Noninvasive brain stimulation during EEG improves machine learning classiResearch square · 2024Article
- An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior.Scientific data · 2024Article
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- Decoding Algorithm of Motor Imagery Electroencephalogram Signal Based on CLRNet Network Model.Sensors (Basel, Switzerland) · 2023Article
- Decoding motor imagery with a simplified distributed dipoles model at source level.Cognitive neurodynamics · 2023Article
- Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models.Sensors (Basel, Switzerland) · 2023Article
- Input Shape Effect on Classification Performance of Raw EEG Motor Imagery Signals with Convolutional Neural Networks for Use in Brain-Computer Interfaces.Brain sciences · 2023Article
- [Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- [Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation.Brain sciences · 2022Article
- Motor Imagery Analysis from Extensive EEG Data Representations Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Deep Residual Convolutional Neural Networks for Brain-Computer Interface to Visualize Neural Processing of Hand Movements in the Human Brain.Frontiers in computational neuroscience · 2022Article
- Empirical comparison of deep learning methods for EEG decoding.Frontiers in neuroscience · 2022Article
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Abstract
A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.
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