ArticleSensors (Basel, Switzerland)2021
An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Dataset Fragmentation, Cognitive Variability, and Reproducibility Challenges in EEG-Based Brain-Computer Interfaces: A PRISMA-Based Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- EEG dataset of consumer- and research-grade systems.Scientific data · 2026Article
- Online Compensation of Systematic Effects in Stimuli Generation for XR-Based SSVEP BCIs.Sensors (Basel, Switzerland) · 2026Article
- A 40-Class SSVEP Speller Dataset: Beta Range Stimulation for Low-Fatigue BCI Applications.Scientific data · 2025Article
- Recent Advances in Portable Dry Electrode EEG: Architecture and Applications in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Review
- [Performance evaluation of a wearable steady-state visual evoked potential based brain-computer interface in real-life scenario].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Article
- A Convolutional Neural Network for SSVEP Identification by Using a Few-Channel EEG.Bioengineering (Basel, Switzerland) · 2024Article
- An open dataset for human SSVEPs in the frequency range of 1-60 Hz.Scientific data · 2024Article
- Multi-frequency steady-state visual evoked potential dataset.Scientific data · 2024Article
- Brain-computer interface: trend, challenges, and threats.Brain informatics · 2023Review
- Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm.Sensors (Basel, Switzerland) · 2023Article
- An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey.Brain sciences · 2023Review
- A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials.Frontiers in neuroscience · 2023Article
- SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods.Frontiers in neuroscience · 2023Article
- An Adaptive Task-Related Component Analysis Method for SSVEP Recognition.Sensors (Basel, Switzerland) · 2022Article
- Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis.Sensors (Basel, Switzerland) · 2022Article
- eldBETA: A Large Eldercare-oriented Benchmark Database of SSVEP-BCI for the Aging Population.Scientific data · 2022Article
- Facilitating Applications of SSVEP-Based BCIs by Within-Subject Information Transfer.Frontiers in neuroscience · 2022Article
- Frequency set selection for multi-frequency steady-state visual evoked potential-based brain-computer interfaces.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.
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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.