ReviewFrontiers in computational neuroscience2022
Status of deep learning for EEG-based brain-computer interface applications.
Review in Frontiers in computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Effects of BCI-based lower limb robots on lower limb function and cognition in stroke patients: a preliminary systematic review.Frontiers in human neuroscience · 2026Pooled it
- Knowledge mapping and research trends of brain-computer interface technology in rehabilitation: a bibliometric analysis.Frontiers in human neuroscience · 2024Pooled it
- MountPat: investigations on the EEG signals.Cognitive neurodynamics · 2026Article
- A Robust Multi-Branch CNN-LSTM Architecture for Cross-Subject Motor Imagery Classification.Sensors (Basel, Switzerland) · 2026Article
- High-Accuracy Detection of Odor Presence from Olfactory Bulb Local Field Potentials via Deep Neural Networks.Sensors (Basel, Switzerland) · 2026Article
- Ultrasound deep learning features for predicting cervical lymph node metastasis in papillary thyroid carcinoma.Frontiers in oncology · 2026Article
- Cognitive state monitoring for neuroadaptive information visualization.Frontiers in human neuroscience · 2026Review
- Closed-loop EEG-based neurofeedback and brain-computer interface interventions for mental health: a review.Frontiers in neuroscience · 2026Review
- Toward zero-calibration MEG brain-computer interfaces based on event-related fields.Biomedical engineering letters · 2026Article
- [Technical maturity and bubble risks of brain-computer interface (BCI): Considerations from research to industrial translation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Brain-Computer Interfaces for Stroke Motor Rehabilitation.Bioengineering (Basel, Switzerland) · 2025Review
- Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.Scientific reports · 2025Article
- Master classes of the tenth international brain-computer interface meeting: showcasing the research of BCI trainees.Journal of neural engineering · 2025Article
- Hybrid BCI for Meal-Assist Robot Using Dry-Type EEG and Pupillary Light Reflex.Biomimetics (Basel, Switzerland) · 2025Article
- Advancements in the application of brain-computer interfaces based on different paradigms in amyotrophic lateral sclerosis.Frontiers in neuroscience · 2025Review
- UET175: EEG dataset of motor imagery tasks in Vietnamese stroke patients.Frontiers in neuroscience · 2025Article
- A Lightweight Network with Domain Adaptation for Motor Imagery Recognition.Entropy (Basel, Switzerland) · 2024Article
- A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.Scientific data · 2024Article
- Decoding Imagined Speech from EEG Data: A Hybrid Deep Learning Approach to Capturing Spatial and Temporal Features.Life (Basel, Switzerland) · 2024Article
- An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks.Brain sciences · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain-computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.
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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.