CBAM-DeepConvNet: Convolutional Block Attention Module-Deep Convolutional Neural Network for asymmetric visual evoked potentials recognition

Purpose: This study aimed to improve the accuracy and the ITR in the stimulative paradigm of character spelling systems based on asymmetric Visual Evoked Potentials (aVEPs) by utilizing EEG signal and an improved Convolutional Block Attention Module-Deep Convolutional Neural Network. Methods: This s...

Volledige beschrijving

Bibliografische gegevens

Hoofdauteurs:	Zhouyu Ji, Shuran Li, Hongfei Zhang, Chuangquan Chen, Qian Xu, Junhua Li, Hongtao Wang
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	Taylor & Francis Group 2025-12-01
Reeks:	Brain-Apparatus Communication
Onderwerpen:	Asymmetric visual evoked potentials (aVEPs) Convolutional Block Attention Module (CBAM) Deep Convolutional Neural Network (DeepConvNet)
Online toegang:	https://www.tandfonline.com/doi/10.1080/27706710.2025.2489396
Tags:	Voeg label toe Geen labels, Wees de eerste die dit record labelt!