ReviewBrain sciences2023
An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey.
Review in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 43 citations in OpenAlex.
- Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2026Review
- A Cheonjiin Layout Mental Speller: Developing a Simple and Cost-Effective EEG-Based Brain-Computer Interface System.Sensors (Basel, Switzerland) · 2026Article
- Electroencephalography-Based Brain-Computer Interface System Using Tongue Movement Imagery for Wheelchair Control.Sensors (Basel, Switzerland) · 2026Article
- Joint MVMD-based optimal feature selection and FW-LS-TWSVM for motor imagery recognition.Scientific reports · 2026Article
- Improved SSVEP Classification Through EEG Artifact Reduction Using Auxiliary Sensors.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence-based analysis of visual electrophysiological signals for clinical interpretation support.Frontiers in neuroscience · 2026Review
- Decoding binocular color differences via EEG signals: linking ERP dynamics to chromatic disparity in CIELAB space.Experimental brain research · 2025Article
- Enhanced SSVEP Bionic Spelling via xLSTM-Based Deep Learning with Spatial Attention and Filter Bank Techniques.Biomimetics (Basel, Switzerland) · 2025Article
- Alertness assessment by optical stimulation-induced brainwave entrainment through machine learning classification.Biomedical engineering online · 2025Article
- Article
- Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.Sensors (Basel, Switzerland) · 2025Review
- A novel scrolling text reading paradigm for improving the performance of multiclass and hybrid brain computer interface systems.PloS one · 2025Article
- [The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG.Bioengineering (Basel, Switzerland) · 2024Article
- Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm.Sensors (Basel, Switzerland) · 2024Article
- FB-CCNN: A Filter Bank Complex Spectrum Convolutional Neural Network with Artificial Gradient Descent Optimization.Brain sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The brain-computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011-2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals.
Indexed as
Identifiers
What OpenQuestion holds
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.