ArticleFrontiers in bioengineering and biotechnology2021
Deep Feature Mining
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed.
- Domain-aware domain-class adaptation network for motor execution to motor imagery EEG classification.Frontiers in neuroscience · 2026Article
- Self-attention bidirectional long Short-Term memory assisted natural language processing on sarcasm detection and classification in social media platforms.Scientific reports · 2025Article
- Decoding decision-making behavior from sparse neural spiking activity.PLoS computational biology · 2025Article
- Stock movement prediction in a hotel with multimodality and spatio-temporal features during the Covid-19 pandemic.Heliyon · 2024Article
- Identification of Methamphetamine Abusers Can Be Supported by EEG-Based Wavelet Transform and BiLSTM Networks.Brain topography · 2024Article
- Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding.Bioengineering (Basel, Switzerland) · 2024Article
- Complex hybrid weighted pruning method for accelerating convolutional neural networks.Scientific reports · 2024Article
- Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces.Frontiers in neuroscience · 2024Article
- Attention-Based DSC-ConvLSTM for Multiclass Motor Imagery Classification.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recognition accuracy and response time are both critically essential ahead of building the practical electroencephalography (EEG)-based brain-computer interface (BCI). However, recent approaches have compromised either the classification accuracy or the responding time. This paper presents a novel deep learning approach designed toward both remarkably accurate and responsive motor imagery (MI) recognition based on scalp EEG. Bidirectional long short-term memory (BiLSTM) with the attention mechanism is employed, and the graph convolutional neural network (GCN) promotes the decoding performance by cooperating with the topological structure of features, which are estimated from the overall data. Particularly, this method is trained and tested on the short EEG recording with only 0.4 s in length, and the result has shown effective and efficient prediction based on individual and groupwise training, with 98.81% and 94.64% accuracy, respectively, which outperformed all the state-of-the-art studies. The introduced deep feature mining approach can precisely recognize human motion intents from raw and almost-instant EEG signals, which paves the road to translate the EEG-based MI recognition to practical BCI systems
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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.