ArticleFrontiers in neuroscience2019
A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
- A multiscale spatio-temporal convolutional attention network for depressive disorder diagnosis via coarse-grained signal reconstruction based on EEG.Scientific reports · 2026Article
- Generative diffusion meets domain adaptation: a framework for EEG cross-subject motor imagery classification.Brain informatics · 2026Article
- Improved motor imagery BCI performance via task-unaware compression in the BELT Bayesian Edge-Cloud architecture.PloS one · 2026Article
- RMETNet: A cross-subject motor imagery EEG signal classification model based on TSLANet and riemannian geometry features.PloS one · 2026Article
- Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding.Sensors (Basel, Switzerland) · 2025Article
- AMANet: a data-augmented multi-scale temporal attention convolutional network for motor imagery classification.Frontiers in neurorobotics · 2025Article
- Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification.Health information science and systems · 2024Article
- Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks.Sensors (Basel, Switzerland) · 2024Article
- Domain adaptation for EEG-based, cross-subject epileptic seizure prediction.Frontiers in neuroinformatics · 2024Article
- A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification.Sensors (Basel, Switzerland) · 2023Article
- 3D Convolution neural network with multiscale spatial and temporal cues for motor imagery EEG classification.Cognitive neurodynamics · 2023Article
- Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion.Brain sciences · 2023Article
- [Multi-scale feature extraction and classification of motor imagery electroencephalography based on time series data enhancement].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals.Diagnostics (Basel, Switzerland) · 2023Article
- Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Convolutional Neural Network with a Topographic Representation Module for EEG-Based Brain-Computer Interfaces.Brain sciences · 2023Article
- Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding.Frontiers in neuroscience · 2023Article
- Local and global convolutional transformer-based motor imagery EEG classification.Frontiers in neuroscience · 2023Article
- Enhancing generalized anxiety disorder diagnosis precision: MSTCNN model utilizing high-frequency EEG signals.Frontiers in psychiatry · 2023Article
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7 authors.
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Abstract
objectiveElectroencephalogram (EEG) based brain-computer interfaces (BCI) in motor imagery (MI) have developed rapidly in recent years. A reliable feature extraction method is essential because of a low signal-to-noise ratio (SNR) and time-dependent covariates of EEG signals. Because of efficient application in various fields, deep learning has been adopted in EEG signal processing and has obtained competitive results compared with the traditional methods. However, designing and training an end-to-end network to fully extract potential features from EEG signals remains a challenge in MI. APPROACH: In this study, we propose a parallel multiscale filter bank convolutional neural network (MSFBCNN) for MI classification. We introduce a layered end-to-end network structure, in which a feature-extraction network is used to extract temporal and spatial features. To enhance the transfer learning ability, we propose a network initialization and fine-tuning strategy to train an individual model for inter-subject classification on small datasets. We compare our MSFBCNN with the state-of-the-art approaches on open datasets.
resultsThe proposed method has a higher accuracy than the baselines in intra-subject classification. In addition, the transfer learning experiments indicate that our network can build an individual model and obtain acceptable results in inter-subject classification. The results suggest that the proposed network has superior performance, robustness, and transfer learning ability.
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