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
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities.
Using Riemannian geometry for SSVEP-based Brain Computer Interface
Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results.
Online SSVEP-based BCI using Riemannian geometry
We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible.
From Euclidean to Riemannian Means: Information Geometry for SSVEP Classification
Brain Computer Interfaces (BCI) based on electroencephalog-raphy (EEG) rely on multichannel brain signal processing.
Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification
In this article we present the results of our research related to the study of correlations between specific visual stimulation and the elicited brain's electro-physiological response collected by EEG sensors from a group of participants.
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli.
Direct information transfer rate optimisation for SSVEP-based BCI
The proposed method shows good performance in classifying targets of a BCI, outperforming previously reported results on the same dataset by a factor of 2 in terms of ITR.
Deep Multi-Task Learning for SSVEP Detection and Visual Response Mapping
jinglescode/ssvep-multi-task-learning
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Our model is able to perform on a calibration-free user-independent scenario, which is desirable for clinical diagnostics.
A Deep Neural Network for SSVEP-based Brain-Computer Interfaces
Objective: Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell.
FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces
pedrorasb/fbcnn
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Objective: To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths.
