Nov 25, 2023 · In this study, a new linear domain adaption approach with experiment-level batch normalization and a single-layer depthwise convolutional neural network is ...
In particular, the experiment-level batch normalization and depthwise convolutional neural network can be integrated as a linear mapping with a scaling ...
In this article, we used electroencephalography (EEG) signals based on Convolutional Neural Network (CNN) model in the deep learning algorithm to identify the ...
Cross-subject EEG linear domain adaption based on batch normalization and depthwise convolutional neural network ... Authors: Guofa Li; Delin Ouyang; Liu Yang ...
We aim to adapt to both domains by specializing batch normalization layers in convolutional neural networks while allowing them to share all other model ...
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Feb 2, 2024 · The aim of this study is to propose deep learning models which can handle this variability and generalize across various patients.
Cross-subject EEG linear domain adaption based on batch normalization and depthwise convolutional neural network . Li, Guofa; Ouyang, Delin; Yang, Liu; Li ...
Cross-subject EEG linear domain adaption based on batch normalization and depthwise convolutional neural network. Guofa Li, Delin ...
Sep 26, 2023 · This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) ...
Dual attentive fusion for EEG-based brain-computer interfaces - PMC
www.ncbi.nlm.nih.gov › PMC9727253
Nov 23, 2022 · This study proposes a Dual Attentive Fusion Model (DAFM) for the EEG-based BCI. DAFM is employed to capture the spatial and temporal information.