SSVEP
16 papers with code • 0 benchmarks • 0 datasets
Classification of examples recorded under the Steady-State Visually Evoked Potential (SSVEP) paradigm, as part of Brain-Computer Interfaces (BCI).
A number of SSVEP datasets can be downloaded using the MOABB library: SSVEP datasets list
Benchmarks
These leaderboards are used to track progress in SSVEP
Most implemented papers
Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training
We transfer this ensemble of fine-tuned DNNs to the new user instance, determine the k most representative DNNs according to the participants' statistical similarities to the new user, and predict the target character through a weighted combination of the ensemble predictions.
A Transformer-based deep neural network model for SSVEP classification
The proposed model validates the feasibility of deep learning models based on Transformer structure for SSVEP classification task, and could serve as a potential model to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.
Short-length SSVEP data extension by a novel generative adversarial networks based framework
yudongpan/tegan
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This study substantiates the feasibility of the proposed method to extend the data length for short-time SSVEP signals for developing a high-performance BCI system.
Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces
This paper presents a source free domain adaptation method for steady-state visually evoked potentials (SSVEP) based brain-computer interface (BCI) spellers.
SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces
cecnl/ssvep-dan
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Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems.
The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
The significance of this study lies in its contribution to establishing a rigorous and transparent benchmark for BCI research, offering insights into optimal methodologies and highlighting the importance of reproducibility in driving advancements within the field.
