Transfer learning and SpecAugment applied to SSVEP based BCI classification
Objective We used deep convolutional neural networks (DCNNs) to classify
electroencephalography (EEG) signals in a steady-state visually evoked potentials (SSVEP) …
electroencephalography (EEG) signals in a steady-state visually evoked potentials (SSVEP) …
FBDNN: Filter banks and deep neural networks for portable and fast brain-computer interfaces
PRAS Bassi, R Attux - Biomedical Physics & Engineering Express, 2022 - iopscience.iop.org
Objective. To propose novel SSVEP classification methodologies using deep neural
networks (DNNs) and improve performances in single-channel and user-independent brain …
networks (DNNs) and improve performances in single-channel and user-independent brain …
[PDF][PDF] FBCNN: a deep neural network architecture for portable and fast brain-computer interfaces
Objective To propose a novel deep neural network (DNN) architecture-the filter bank
convolutional neural network (FBCNN)-to improve SSVEP classification in single-channel …
convolutional neural network (FBCNN)-to improve SSVEP classification in single-channel …
Optimized Complex Convolutional Neural Network for Steady-State Visual Evoked Potentials Signal classification
Q Li, C Han, Y Liu, J Liu - 2024 IEEE 25th China Conference on …, 2024 - ieeexplore.ieee.org
The steady-state visual evoked potential (SSVEP) is one of the most commonly used
paradigms in the BCI system, which produces a significant response to visual stimuli of a …
paradigms in the BCI system, which produces a significant response to visual stimuli of a …