ArticleBMC bioinformatics2018
Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Objective Emotion Assessment Using a Triple Attention Network for an EEG-Based Brain-Computer Interface.Brain sciences · 2025Article
- Temporal convolutional transformer for EEG based motor imagery decoding.Scientific reports · 2025Article
- MCTGNet: A Multi-Scale Convolution and Hybrid Attention Network for Robust Motor Imagery EEG Decoding.Bioengineering (Basel, Switzerland) · 2025Article
- Advancing BCI with a transformer-based model for motor imagery classification.Scientific reports · 2025Article
- Data Uncertainty (DU)-Former: An Episodic Memory Electroencephalography Classification Model for Pre- and Post-Training Assessment.Bioengineering (Basel, Switzerland) · 2025Article
- Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding.Sensors (Basel, Switzerland) · 2025Article
- Recognition of brain activities via graph-based long short-term memory-convolutional neural network.Frontiers in neuroscience · 2025Article
- A composite improved attention convolutional network for motor imagery EEG classification.Frontiers in neuroscience · 2025Article
- MSHANet: a multi-scale residual network with hybrid attention for motor imagery EEG decoding.Cognitive neurodynamics · 2024Article
- Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification.Health information science and systems · 2024Article
- An effective classification approach for EEG-based motor imagery tasks combined with attention mechanisms.Cognitive neurodynamics · 2024Article
- CTNet: a convolutional transformer network for EEG-based motor imagery classification.Scientific reports · 2024Article
- Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification.Frontiers in neurorobotics · 2024Article
- Deep temporal networks for EEG-based motor imagery recognition.Scientific reports · 2023Article
- Decoding Algorithm of Motor Imagery Electroencephalogram Signal Based on CLRNet Network Model.Sensors (Basel, Switzerland) · 2023Article
- Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion.Brain sciences · 2023Article
- Transformer-Based Network with Optimization for Cross-Subject Motor Imagery Identification.Bioengineering (Basel, Switzerland) · 2023Article
- Cross-subject aesthetic preference recognition of Chinese dance posture using EEG.Cognitive neurodynamics · 2023Article
- Survey on the research direction of EEG-based signal processing.Frontiers in neuroscience · 2023Review
- Application of multi-task transfer learning: The combination of EA and optimized subband regularized CSP to classification of 8-channel EEG signals with small dataset.Frontiers in human neuroscience · 2023Article
Corrections and comments
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3 authors.
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Abstract
backgroundConventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers.
methodsAlternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals.
resultsExperimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets.
conclusionBy introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals.
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